Integrated inertial/GPS navigation system

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First Claim
1. A method for adjusting the phase and frequency of a received GPS signal in an inertialGPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver, the GPS receiver having an input port for inputting a deltaphase at deltatime intervals into a received satellite signal, the GPS receiver having an output port for outputting a carrier phase error, the carrier phase error being the difference between the actual phase of the received GPS signal and a reference value, the GPS receiver having an output port for outputting a pseudorange associated with the received GPS signal, the IMU having an output port for outputting acceleration and angular velocity of the IMU, the deltaphase being the sum of at least two deltaphase components, the method comprising the step:
 (a) determining a Kalman deltaphase component, a plurality of candidate Kalman deltaphase components being obtained by performing a first set of more than one Kalman filter processes, the inputs to each firstset Kalman filter process including the carrier phase error and a position vector associated with the IMU, a candidate Kalman deltaphase component being produced as an output of each firstset Kalman filter process, the Kalman deltaphase component being determined from a candidate Kalman deltaphase components selected from the plurality of candidate Kalman deltaphase components by applying a predetermined Kalman deltaphase component selection rule;
(b) determining an IMU deltaphase component from the IMU outputs and a directioncosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertialGPS navigation system.
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Abstract
The invention is a method and apparatus for adjusting the phase and frequency of a received GPS signal in an inertialGPS navigation system by inputting a deltaphase at deltatime intervals into the received satellite signal. The method comprises the steps of determining two deltaphase components: (a) a Kalman deltaphase component derived from a plurality of candidate Kalman deltaphase components obtained by performing a first set of more than one Kalman filter processes and (b) an IMU deltaphase component derived from the IMU outputs and a directioncosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertialGPS navigation system.
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22 Claims
 1. A method for adjusting the phase and frequency of a received GPS signal in an inertialGPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver, the GPS receiver having an input port for inputting a deltaphase at deltatime intervals into a received satellite signal, the GPS receiver having an output port for outputting a carrier phase error, the carrier phase error being the difference between the actual phase of the received GPS signal and a reference value, the GPS receiver having an output port for outputting a pseudorange associated with the received GPS signal, the IMU having an output port for outputting acceleration and angular velocity of the IMU, the deltaphase being the sum of at least two deltaphase components, the method comprising the step:
(a) determining a Kalman deltaphase component, a plurality of candidate Kalman deltaphase components being obtained by performing a first set of more than one Kalman filter processes, the inputs to each firstset Kalman filter process including the carrier phase error and a position vector associated with the IMU, a candidate Kalman deltaphase component being produced as an output of each firstset Kalman filter process, the Kalman deltaphase component being determined from a candidate Kalman deltaphase components selected from the plurality of candidate Kalman deltaphase components by applying a predetermined Kalman deltaphase component selection rule;
(b) determining an IMU deltaphase component from the IMU outputs and a directioncosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertialGPS navigation system.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
 12. Apparatus for adjusting the phase and frequency of a received GPS signal in an inertialGPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver, the GPS receiver having an input port for inputting a deltaphase at deltatime intervals into a received satellite signal, the GPS receiver having an output port for outputting a carrier phase error, the carrier phase error being the difference between the actual phase of the received GPS signal and a reference value, the GPS receiver having an output port for outputting a pseudorange associated with the received GPS signal, the IMU having an output port for outputting acceleration and angular velocity of the IMU, the deltaphase being the sum of at least two deltaphase components, the apparatus comprising:
(a) a processor for determining a Kalman deltaphase component, a plurality of candidate Kalman deltaphase components being obtained by performing a first set of more than one Kalman filter processes, the inputs to each firstset Kalman filter process including the carrier phase error and a position vector associated with the IMU, a candidate Kalman deltaphase component being produced as an output of each firstset Kalman filter process, the Kalman deltaphase component being determined from a candidate Kalman deltaphase components selected from the plurality of candidate Kalman deltaphase components by applying a predetermined Kalman deltaphase component selection rule;
(b) a processor for determining an IMU deltaphase component from the IMU outputs and a directioncosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertialGPS navigation system.  View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
1 Specification
This application claims the benefit of U.S. Provisional Application No. 60/199,897, filed Apr. 26, 2000.
(Not applicable)
This invention relates generally to integrated inertial/GPS navigation systems and more specifically to integrated inertia/GPS navigation systems that achieve improved operational reliability through the use of a plurality of Kalman filters.
The Autonomous Integrity Monitored Extrapolation (AIME™) algorithm is an improved algorithm for integrating the Global Positioning System (GPS) with an inertial navigation system (INS) in a way that the navigation solution has a minimum susceptibility to equipment failures, interference, and jamming. The principal problem with using Kalman filters to integrate GPS with INS is that a slow GPS failure due to interference or spoofing can contaminate the integrated solution before it is detected. The AIME™ algorithm solves this problem by analyzing the residuals of the filter over long time intervals before the corrections are added externally to the INS solution. Because the solution is based on the continuous INS output, it is extremely reliable, and continuous in case of complete loss of the GPS. By using parallel solutions which are not sensitive to the failure, it rejects the contamination and continues using an uncontaminated, reliable precision navigation solution.
This principle is applied in the present invention to carrier tracking. The basis of the invention is the AntiJam AIME™ (AJAIME™) algorithm. This algorithm achieves at least 20 dB J/S improvement, and possibly 3040 dB J/S improvement. This is in addition to the J/S improvements from other techniques. Previous solutions to improve J/S performance are based on deep coupling using a vector solution combining both carrier and code measurements and INS errors. These errors are estimated in a large, complex maximum likelihood estimator. One problem with these approaches is that the signals are no longer independent so that receiver autonomous integrity monitoring (RAIM) cannot be used to determine integrity.
The AJAIME™ approach avoids the loss of integrity and possible contamination inherent in such vector approaches. Even if jamming is successful, the AIME™ solution provides maximum accuracy for coasting with the calibrated INS solution, since this solution is uncontaminated from the period when jamming or interference first began.
For manned aircraft or long range missiles already equipped with a highquality INS, the AIME™ algorithm can be implemented in a separate, very inexpensive small box, which adds corrections to the INS solution. For inexpensive short range missiles, or munitions, without INS, the AIME™ algorithm is combined with MEMS technology. This solution uses 1 deg/hr silicon gyros, 20 microg accelerometers, and a GPS receiver, all integrated in an inexpensive, small box.
Without WAAS, the failure detection and exclusion (FDE) availability of RAIM for nonprecision approach (NPA) using 24satellite constellations with up to three outages is less than 50%. Using WAAS for Precision Approach CAT I (DH=200 feet), the FDE availability of RAIM is less than 2%. One objective of this invention is to improve FDE availability by up to five orders of magnitude. RAIM is basically a “snapshot” approach using instantaneous noisy position data. Instead of RAIM, this invention uses an integrated systems approach. With today'"'"'s system technology, including microelectromechanical system (MEMS) technology, and using Kalman filter algorithms, it is possible with this approach to use feedback from rate, acceleration, acceleration rate, etc., up to at least the fourth derivative of position, in the Kalman filter error model.
In order to reject errors caused by satellite clock failures, multipath, excessive atmospheric errors, or subsystem failures, these errors and all other errors are modeled in a bank of Kalman filters in the AIME™ algorithm. Each of these filters is tuned for excessive errors in one channel, or in one group of channels, or in a subsystem. The filter which models the errors correctly will have uncorrelated residuals, because of the “innovations property” of Kalman filters. By estimating the mean of the residuals over many Kalman filter cycles, a test statistic is obtained for each filter. This statistic is the meansquare estimated residual for that filter. The filter with the correct model will have the lowest test statistic and the output of that filter will be used. The other filters have large meansquare estimated residuals, since they do not correctly model the excessive error.
The word “failure” is used herein to represent any excessive error which can contaminate the solution, whether due to an actual subsystem soft failure or due to a temporary excessive instrument error or signal error.
Recursive algorithms are used in order that the estimate can use very large numbers of Kalman filter cycles to detect slow failures. In present civil applications, these estimates use residuals over the past two hour period to detect and exclude errors due to slow satellite clock drifts. This algorithm has been certified for Primary Means and has been flown on commercial airliners for many years. In addition, many years of data have been collected and analyzed from flights in normal airline operation.
To improve antijam performance, other approaches have claimed that 3040 dB J/S improvement can be achieved by a vector processing algorithm using deep coupling, where the INS is integrated with the measurements from both the carrier and code discriminators. This would be done in a large, complex, maximumlikelihood estimator which attempts to estimate all error sources including INS errors. This approach creates an integrity problem since the different satellite measurements are no longer independent, and RAIMtype integrity checking is no longer valid.
There are three additional difficulties with this approach: (1) it does not consider the fundamental problem that arises when integrating inertial information with carrier phase information, (2) it does not properly consider the unique characteristics of inertial system errors, and (3) it does not consider the very different characteristics of carrier phase errors vs. code errors.
The fundamental problem referred to in (1) above is that the wavelength of the carrier is only 19 centimeters. During turbulence or high dynamics, the separation between the INS and the GPS antenna makes it difficult to process changing inertial attitude information fast enough to aid the carrier tracking at the antenna to centimeter accuracy at 1000 Hz without excessive time lags.
The unique characteristics of inertial systems referred to in (2) above arise because inertial errors vary slowly. Gyro and accelerometer errors change at only a fraction of a microg per second. This causes phase errors of only a few micrometers per second. This is too small to estimate with short term phase measurements, which are noisy. It is much better to estimate these errors over long periods of time using PR measurements, which have bounded position errors over these long periods.
The difficulty identified in (3) above arises because pseudorange measurements are made at 1 Hz with an accuracy of meters while the carrier phase is measured at 1000 Hz with an accuracy of a centimeter or less. It is not practical to combine these measurements in the same Kalman filter. If they are combined, it is almost impossible to prevent contamination from individual signals.
The solution to (1) above (i.e. aiding the carrier discriminators in turbulence and dynamics) is to use strapdown measurements in body axes at 400 to 1600 Hz to compute the antenna position relative to the INS. These measurements are used to estimate antenna position a short time in the future at 1000 Hz. The short prediction time is adjusted to cancel time lags in the system. This effectively eliminates the dynamic stress that the carrier loop has to track.
The solution to (2) above (i.e. inertial characteristics) is to use only pseudorange information in a navigation Kalman filter with a large step size, such as 20 seconds or more, to estimate INS errors. These estimated corrections are added to the INS solution to obtain the 1000 Hz extrapolator aiding information for the carrier discriminators.
The solution to (3) above (i.e. carrier vs. code characteristics) is to use a separate AJAIME™ Kalman filter for the carrier discriminator information. This Kalman filter has a much smaller update frequency of 1 Hz., to estimate slow corrections to the 1000Hz phase discriminator data. The inertial extrapolator has already eliminated the dynamics from the measurements, and the navigation Kalman filter has eliminated slowly varying errors, such as user clock errors, atmospheric errors, and IMU instrument errors. The 1Hz AJAIME™ Kalman filter will only have to correct small, residual mediumfrequency errors in the carrier tracking loop. This 1Hz carrier loop filter will also use parallel banks of similar Kalman filters with residuals averaged over short and long periods to detect and reject phase error contamination.
Measurements for different satellites are no longer independent at the same update cycle, but this was never a requirement for AIME™ integrity as it is for RAIM integrity. In fact, the AIME™ residuals are highly correlated at each Kalman measurement cycle, but they are uncorrelated from one cycle to the next, provided there is no failure, and the model is correct.
In military applications, the AIME™ navigation Kalman filter cycle time can be reduced from 60 seconds, as used in civil applications, to 20 seconds. This navigation Kalman filter is then used to aid a second ANTIJAM AIME™ (AJAIME™) Kalman filter with cycle time of only one second. This filter in turn aids an inertial extrapolator, operating at 1000 Hz., which is used to eliminate the high frequency dynamics from the carrier tracking loop. This provides J/S improvement of 3040 dB. The objective is to reject multipath, unintentional anomalies or interference, jamming, and spoofing, and to continue carrier tracking for more than a minute with complete loss of the GPS signal, as caused by masking due to aircraft or missile motion.
Definitions of symbols used herein are as follows.
A_{x}, A_{y}, A_{z}, Nongravitational accelerations along navigation axes
ADIRS Air Data Inertial Reference System
AHRS Attitude and heading reference system
AIME™ Autonomous integrity monitored extrapolation
AJAIME™ AntiJam autonomous integrity monitored extrapolation
[C^{R}_{B}] Direction cosine matrix from Body to navigation Reference axes
CAT I Category of precision approach with 200 foot decision height
DGPS Differential Global Positioning System
DH Decision height for approach to landing
FDE Failure detection and exclusion
FOM Figure of Merit
HDOP Horizontal Dilution of Precision
HIL Horizontal integrity level
HPL Horizontal protection level
IMU Inertial measurement unit
INS Inertial navigation system
IRS Inertial reference system
J/S Ratio of jamming power to signal power, usually measured in dB
MEMS Microelectromechanical system
NPA Non precision approach
OCS Operational Control Segment
PR Pseudo Range measurement from GPS
R Earth radius
R_{L}^{B }Lever arm from IMU to GPS antenna in body axes
RAIM Receiver autonomous integrity monitoring
SA Selective Availability
VDLL Vector delay lock loop
VPL Vertical protection level
WAAS Wide Area Augmentation System
deg/hr angular rate unit of degrees per hour
g unit of acceleration equal to 32.2 feet/second^{2 }
{overscore (ω)} Total spatial angular rate of navigation coordinates (earth rate+craft rate)
τ Correlation time of first order Markov process
Definitions of Kalman filter symbols used herein are as follows.
KF Kalman filter
k Index of Kalman filter cycle, or navigation solution cycle
M,N Dimension of measurement vector, error state vector, resp.
r_{k }Residual vector of Kalman filter, dimension (M×1)
V_{k }Covariance matrix of residuals, dimension (M×M)
r_{est }Estimated mean residual vector of Kalman filter, dimension (M×1)
V_{est }Covariance matrix of estimated mean residual vector, dimension (M×M)
s_{est}^{2 }Test statistic
z_{k }Measurement vector of Kalman filter
H_{k}, F Observation matrix (M×N), dynamics matrix N×N), resp.
x_{k}^{−}, x_{k}^{+} Error state vector before, and after, update, resp., dimension (N×1)
R_{k}, Q_{k }Measurement noise (M×M), Process noise N×N), resp.
P_{k}^{−}, P_{k+} Error state Covariance matrix before, and after update, dimension (N×N)
K_{k }Kalman gain matrix, dimension (N×M)
T Superscript indicating transpose of matrix
−1 Superscript indicating inverse of matrix
z_{i}(t) Measurement from i^{th }satellite at time t
x_{z}(t) Temporary error state used to make measurement at time t
PR_{ic}(x_{z}(t)) Pseudorange computed from error state x_{z}(t)
PR_{im}(t) Pseudorange measured at time t
Φ(t−t_{k}) Transition matrix from time t_{k }to time t
eφ_{ic}(x_{z}(t)) Computed phase errors at time t
eφ_{im}(t) Measured phase errors at time t
The invention is a method and apparatus for adjusting the phase and frequency of a received GPS signal in an inertialGPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver where the GPS receiver is postulated as having an input port for inputting a deltaphase at deltatime intervals into a received satellite signal and an output port for outputting a carrier phase error defined as the difference between the actual phase of the received GPS signal and a reference value. The GPS receiver is also postulated as having an output port for outputting a pseudorange associated with the received GPS signal. The IMU is postulated as having an output port for outputting the acceleration and angular velocity of the IMU.
The method comprises the steps of determining two deltaphase components: (a) a Kalman deltaphase component and (b) an IMU deltaphase component. The Kalman deltaphase component is derived from a plurality of candidate Kalman deltaphase components obtained by performing a first set of more than one Kalman filter processes. The inputs to each firstset Kalman filter process include the carrier phase error and a position vector associated with the IMU. A candidate Kalman deltaphase component is produced as an output of each firstset Kalman filter process. The Kalman deltaphase component is determined from a candidate Kalman deltaphase components selected from the plurality of candidate Kalman deltaphase components by applying a predetermined Kalman deltaphase component selection rule.
The IMU deltaphase component is derived from the IMU outputs and a directioncosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertialGPS navigation system.
FIG. 1 shows an embodiment of the invention in the context of a GPS receiver and an IMU.
FIG. 2 shows an embodiment of that portion of the invention that transforms present and past IMU acceleration and angular velocity into estimates of future values of IMU acceleration and angular velocity.
The AIME™ invention described herein applies to both the AIME™ navigation Kalman filter (KF), and to the AJAIME™ carrierloop Kalman filter (KF). The AIME™ navigation KF has been used by itself in the past for navigation of commercial airliners. Details of this filter can be found in U.S. Pat. No. 5,583,774 which is incorporated herein by reference.
In a typical application to prevent jamming, the AIME™ navigation KF is used both for navigation and to aid the AJAIME™ carrierloop KF. The AIME™ navigation KF typically operates at Kalman update rates of 1 cycle per minute when using 0.01 deg/hr inertial grade gyros and 3 cycles per minute or higher when using 0.1 deg/hr to 1 deg/hr attitude and heading reference system (AHRS) grade gyros. The AJAIME™ carrierloop KF typically operates at a Kalman update rate of 1 Hz. The AIME™ navigation KF estimates lowfrequency error states with correlation times of more than 5 minutes. The AJAIME™ carrierloop KF estimates mediumfrequency errors with correlation times of 5 minutes or less. Muchhigherfrequency errors are caused by aircraft dynamics. These dynamic errors are corrected by an inertial extrapolator which operates at 1000 Hz. The extrapolator uses IMU measurements at 200 Hz to 1600 Hz.
The AIME™ algorithm applies to the AIME™ navigation KF and the AJAIME™ carrierloop KF separately. In each KF, the algorithm is used to detect and exclude excessive errors in the frequency range of its model and to exclude contamination or spoofing. This makes carrier tracking less vulnerable to interference, jamming, or spoofing, and less vulnerable to temporary loss of the GPS signal caused by masking due to aircraft angular motion.
The equations for the test statistic used in the AIME™ algorithm are based on the optimal estimation of a hypothesized true residual, based on observations of the measured residual over many Kalman filter cycles. This is the essence of the invention.
It is assumed that the individual Kalman filter residuals are independent measurements of the true residual. They differ from the true residual because of measurement noise, which is the innovation in the Kalman filter caused by process noise and GPS measurement noise. The sequence of these measurement residuals over a long time period is used to estimate the true residual. This sequence is the innovations sequence of the Kalman filter.
AIME™ detects and identifies failures by estimating the mean of the residuals in the Kalman filter over various time intervals. If the Kalman filter model is correct (no failures), the residuals are uncorrelated over time (innovations principle) and the mean of the residuals is zero. The estimate of the mean approaches zero as longer time intervals, represented by more Kalman filter cycles, are used in the estimate.
Large failures can be detected quickly by estimating the mean residual over a few cycles. Slow failures are the most difficult to detect. The more Kalman filter cycles used in estimating the mean residual, the smaller the failure that can be detected, without excessive false alarms. This invention includes a recursive algorithm which makes it possible to use an extremely large number of Kalman filter cycles in the estimate of the mean residual without excessive use of computer memory or processing capacity. In the preferred embodiment described below, 120 Kalman filter cycles are used. However, 256, 1024, or an even greater number of cycles could be used, thereby making it practically impossible for an adversary or terrorist to “spoof” the system.
In addition to using the increased number of cycles to increase the averaging time used in the estimate, it is used to reduce the Kalman filter update interval. A smaller update interval is desirable, in order to improve the performance when IMU instrument errors are larger than normal.
The recursive algorithm computes the estimated residual over various time intervals by adding single terms to running sums. The individual terms, indexed by subscript “k”, are separated into subgroups of several Kalman filter cycles each. Because of the associative law of addition, the sums for each subgroup can be determined first, and then the sums for the subgroups can be summed to determine the sum for the total. The total sum is formed by starting with the first subgroup sum, and adding one new term at a time to a running sum of subgroup sums, as explained in the preferred embodiment.
If a failure occurs, the Kalman filter model is incorrect, and the residuals of the Kalman filter are no longer uncorrelated from one cycle to the next. In this case, the failure detection statistic is noncentral Chisquare distributed, and it exceeds a threshold, which can be computed for a maximum misseddetection probability.
Isolation is accomplished by using a bank of parallel hypothesis test Kalman filters, each tuned for a different failure. There is a separate filter for each satellite failure or group of satellite failures, for satellite clock failures, for IMU instrument failures, for GPS user clock failures, for altimeter failures, or for the onset of jamming. Each of these filters saves its residuals over long time intervals.
Navigation can continue despite IMU or altimeter soft failures, by using a solution returned for large IMU errors, or large altimeter errors, respectively. The same is true for large GPS user clock errors. If cost permits, redundant systems can also be used even in the event of hard failures. Redundant IMUs, altimeters, and GPS receivers are typically used in commercial airliner applications.
When there is a failure, as detected by the single Kalman filter which is modeled for no failures, the corresponding long term residuals of the different failure hypothesis test filters are analyzed. The test filter which is modeled for the particular failure will still have small, uncorrelated residuals over the entire interval, and a small test statistic. All the other test filters will have correlated residuals and large test statistics.
The squared estimated mean residual is transformed and normalized. This is done by using the inverse of the covariance matrix V_{est }of the estimated mean residual r^{est }to form a statistic s_{est}^{2}. If there are no failures, the mean of the residuals is zero, and the statistic s_{est}^{2 }is central Chisquare distributed. If there are failures, the mean of the residuals is nonzero, and the statistic s_{est}^{2 }is noncentral Chisquare distributed.
In both civil and military applications, invulnerability to intentional jamming is highly desirable. The objective is 3040 dB J/S improvement. The AJAIME™ algorithm differs from a vector delay lock loop (VDLL) approach or other deep integration approaches using all measurements to aid each tracking loop in coupled signaltracking channels. These approaches use large, complex, maximumlikelihood estimation techniques to calibrate carrier, code, and IMU errors all in the same filter. As a result, the signals are no longer independent, and integrity algorithms such as RAIM cannot be used to determine interference or spoofing. In addition, individual signal lock is difficult to detect. Because of the dynamic stress in high performance weapons systems, effective bandwidths are still too wide to provide significant J/S improvement.
The AJAIME™ approach solves this last problem by using inflight calibrated deltatheta, deltaV IMU measurements in body axes to predict both body linear and angular position at a high rate. This makes it possible to extrapolate antenna position ahead of real time at 1000 Hz in navigation axes. The position is then resolved along lineofsight to each satellite to obtain carrier phase and frequency for generating phase rotation for Doppler removal in each channel ahead of real time. It will be shown that this makes the system relatively unaffected by extremely high rates of change of linear or angular acceleration, commonly called “jerk”. In effect, the highfrequency dynamics called “stress” is removed before the tracking loop is closed.
Since the bandwidth of the carrier tracking loops is normally determined by the high frequency dynamics, the bandwidth is now determined only by additional residual errors. These residual errors are changes in user clock frequency, changes in atmospheric delays, multipath changes, and gravity anomaly changes. Additional slow corrections for these changes are estimated by the outputs from one of a bank of high frequency (1 Hz.) Kalman filters. The filter output used is the filter which most closely models the errors. The measurements to these filters are the average carrier phase error, after the high frequency dynamics, user clock errors, multipath errors, and atmospheric errors have already been removed or estimated.
Instead of using the Costas filter, the loop is closed by the extrapolator, together with these small Kalman filter corrections. Each of these parallel Kalman filters is tuned for a different excessive contamination effect. The particular output used is the one with the minimum AIME™ test statistic. The Kalman filter with the best model has uncorrelated residuals. This is the “innovations property” of Kalman filters. The uncorrelated residuals result in the minimum test statistic. In effect, the AIME™ algorithm uses adaptive Kalman filter principles, but with no time lag. This not only solves the integrity problem, but makes it almost impossible to spoof. The spoofer would have to modify each signal to correspond to the effect on every channel of a small change in user clock frequency, an IMU sensor error, a small multipath error, or an atmospheric error. Each simulated error would be too small to drag the solution off.
These highfrequency (1 Hz) AJAIME™ carrierloop Kalman filters are aided by one of a bank of low frequency (1 to 3 cycles per minute) AIME™ navigation Kalman filters. These AIME™ navigation filters use prefiltered 1Hz GPS PR measurements to estimate the low frequency or slow “DC” errors, such as navigation axis misalignments, gyro and accelerometer bias errors, lowfrequency user clock and frequency errors, and slow atmospheric offsets for each satellite. IMU instrument misalignments are stable, and they can be calibrated at the factory to an accuracy of 1 arc second, so that they do not have to be reestimated by the navigation filter.
Because of the IMU, the response to real dynamic motion is almost instantaneous. However, the Kalman filters act as low pass filters to GPS signal errors. The time constants of these AIME™ Kalman filters is at least 1 minute using 1 deg/hr gyros, and at least 5 minutes, using 0.1 deg/hr gyros. The time constants of conventional carriertracking loops is 8 milliseconds for a lowdynamics civilaviation 20Hz loop, and approximately 3 milliseconds for a militaryaviation 50Hz loop. It is concluded that J/S improvement is 30 to 40 dB, compared to a 20Hz conventional carriertracking loop when AIME™ uses 0.1 deg/hr gyros or compared to a 50Hz tracking loop, when AIME™ uses 1 deg/hr gyros.
The equations for the estimated residual r_{est}, its covariance V_{est}, and its normalized sumsquared statistic s_{est}^{2 }are given below.
The measurement residual r_{k }and its covariance V_{k }at each Kalman filter cycle k is given by
and
The inverse of the covariance matrix (information matrix) of estimated mean is given by
The sum of weighted residuals is given by
The estimated mean and normalized sumsquared test statistic are given by
The equations are performed in single precision in order to reduce the computer duty cycle requirements and memory requirements. The duty cycle requirements are a result of the many inversions and reinversions of the covariance matrices of the measurement residuals, which are required whenever the satellites in use are changed, as explained in the next section. The memory requirements result from the many matrix inverses which must be saved for the multiple averaging which is explained in the next sections.
The Kalman filter equations are given below. These equations are similar to those in Gelb, Applied Optimal Estimation, TASC, MIT Press, 1974, Table 4.21, page 110. The equations are performed in double precision in order that the error state covariance matrix remain positive definite.
The measured difference between computed and measured PR at 1 Hz, for satellite i is given by
The measured difference between computed and measured carrier phase is given by
z_{i}(t)=eφ_{ic}(x_{z}(t))−eφ_{im }(t) where x_{z}(t)=Φ(t−t_{k}) x_{z}(t_{k}) (2.1N)
The averaged or “prefiltered” measurement at k is given by
The Kalman filter update is accomplished using the Kalman filter gain K_{k}, the state estimate update x_{k}^{+}, and the error covariance update P_{k}^{+}.
Kalman filter propagation is accomplished using the state estimate extrapolation x_{k+1}^{−} and the error covariance extrapolation P_{k+}.
Unlike Gelb, the propagation is done after the update. This is necessary because the updates are done on the error state x_{k}^{−} after the measurements z_{k }are averaged or prefiltered at 1 Hz between k and k+1, as indicated in Equation (2.2). The Kalman filter updates of x_{k}^{−} are actually performed in the background during the interval from k+1 to k+2. In order to begin collecting measurements during this interval, before x_{k+1}^{−} has been computed, a temporary estimate x_{z}(t_{k+1}) is used to compute the z_{i}(t) during this interval, as indicated in Equation (2.1). The measurements z(k+1) are then adjusted for the difference
as indicated in Equation (2.4) with k replacing k+1 at the next cycle.
Modern receivers can accommodate up to 12 satellites in view. With the present constellation of 24 satellites, there are typically an average of 8 of the original 24 satellites in view, and sometimes as few as 6. Spare satellites are of little use in improving geometry, since they are spaced close to one of the original satellites most likely to fail. It is unnecessary to use more than 8 satellites when the geometry is good.
As an extreme example of bad geometry, the lineofsights to seven of the satellites could all lie in a plane, and the lineofsight to the eighth satellite could be perpendicular to that plane. The failure of the eighth satellite would be undetectable using only the original eight satellites. If one of the more than eight satellites used was an additional satellite also perpendicular to the plane, the satellite failure would be detectable by using this additional satellite.
When there are more than 8 in view, only 8 satellites with good geometry are used. These 8 satellites are selected by computing the RAIM protection level for the set of 8 satellites. The horizontal protection level (HPL) would be used to select for horizontal accuracy, while the vertical protection level (VPL) would be used for applications like precision approach, where vertical accuracy is critical.
Initially, the first 8 satellites in view are used in the table. As explained previously, the RAIM protection level is used to determine bad geometry. If the 8 satellites used have a computed protection level exceeding the average level with 8 satellites, one of the satellites is replaced by one of the satellites not used, and the RAIM protection level is recomputed with the new set. If the new set of 8 satellites has larger protection level than the previous set of 8, the newer set is used. If there were more than 9 satellites in view, this process is repeated until the specified average protection level is achieved.
The decision must be made on which satellites to replace, when the protection level is too large with the presently selected 8 satellites, and more than 8 satellites are in view. The RAIM protection level is determined by computing the “slope” for each of the satellites. This slope is determined for each of the satellites used, by computing the least squares position error solution. Then the position error and test statistic corresponding to a fixed bias error in that satellite is determined. The slope for that satellite bias error is the ratio of the position error resulting from the fixed bias error to the test statistic resulting from the fixed bias error.
The RAIM protection level is determined from the satellite error which results in the maximum slope, since that is the most difficulttodetect satellite. This means that when this satellite has a large bias error, it also has the largest position error when the test statistic exceeds the threshold. This implicitly means that it has the least redundancy checking from other satellites. The satellite to be replaced is the satellite with the minimum slope, since this is the mosteasytodetect satellite. This means it has the most redundancy from other satellites, and can be rejected with little loss of redundancy for integrity checking.
The satellite information is kept in a table, with 8 rows, corresponding to a maximum of 8 satellites used. The rows are filled, one row at a time, as new satellites first come into view. The first column of the table shows the ID Number of the satellite. This column vector is referred to as the “current map”. When a satellite goes out of view, the corresponding row is cleared, and a “−1” is used to replace the satellite ID number. If a new satellite replaces the old in this row, the new satellite ID number replaces the old. The other columns in the table contain information on the status of the satellite, such as how long it has been in view.
The two sums Σ_{k}V_{k}^{−1 }and Σ_{k }V_{k}^{−1}r_{k }used in the estimated mean in Equations (1.3) and (1.4) respectively are taken over many Kalman filter cycles as explained above. These sums are performed as running sums by starting with the first term, and adding one new term to the running sum at each cycle.
Both the covariance matrix V_{k }from Equation (1.2) and its inverse V_{k}^{−1 }as used in Equation (1.3) are stored in full 8×8 matrices even though there are missing satellites. The corresponding row and column for a missing satellite, as indicated by the “−1” in the map, are filled with zeroes.
The residual vector r_{k }in Equation (1.1), the weighted residual vector V_{k}^{−1}r_{k}, the running sum of weighted residuals Σ_{k}V_{k}^{−1}r_{k }in Equation (1.4), and the estimated mean residual rest in Equation (1.5) are each stored in a full 8×1 column matrix, even though the number of satellites used is less than 8. The missing satellites in this matrix are replaced by zeroes corresponding to the “−1” flags from the current map which is also 8×1.
The individual terms, indexed by subscript “k”, can be separated into subgroups of several Kalman filter cycles each. Because of the associative law of addition, the sums for each subgroup can be determined first and then the sums for each subgroup can be summed to determine the sum for the total. The total sum is formed by starting with the first subgroup sum, and adding one new term at a time to a running sum of subgroup sums, as explained in the next section.
The satellites used in a cycle or group are represented by a reference map for that cycle or group. The map for the current Kalman filter measurement is simply the first column of the current satellite table, showing the ID numbers of the satellites used or the “−1” indicating no satellite in that position of the table. The reference map for a group of cycles is adjusted starting with the reference map for the first cycle or subgroup, as explained below When adding a current new term to a running sum, a special procedure, described below, is required when the current term uses a different set of satellites (represented by the current map for that term), from the set of satellites used by the running sum up to that term (represented by the reference map for that running sum).
This map difference could be caused by a satellite going out of view, as determined by a count of the number of consecutive Kalman filter cycles for which the measurement for that satellite is missing. The satellite is assumed to be out of view if this count equals six or more. If less than six, as due to masking during a turn, it is assumed that the satellite is still in use, although its measurement is missing temporarily. In this case, the reference map for the running sum is unchanged in the procedure below. The map difference also could be caused because of a previously used satellite being replaced by a new satellite, or because a new satellite comes into view in an unoccupied position in the table.
These differences may also cause the reference maps for subgroups to differ. When summing the sums for each subgroup, a similar procedure is required when the new subgroup sum uses a different set of satellites (represented by the current map of that subgroup sum), than that used by the running sum of subgroup sums up to that point (represented by the reference map of that running sum of subgroup sums).
When the maps differ, the running sums must be modified to correspond to the satellites used in the new term before adding the new term. Since the sums in Equations (1.3) and (1.4) represent information matrices, and weighted residuals, with zero rows and columns for missing is satellites, it is not clear how to modify the running sums before adding the new term.
The answer is to first compress the 8×8 information matrix and the running sum of weighted residuals to compact matrices with no zeroed rows or columns. The dimensions are determined by the number of measurements used in the running sums at this point before adding the new term. Since there are no zeroed rows and columns in the compressed information matrix, it can be inverted to obtain the corresponding covariance matrix called V_{avg}. This is the covariance matrix of the estimated residual r_{avg }based on the measurements up to that point in the running sum.
The estimated residual r_{avg }up to that point can also be computed as r_{est }from Equation 1.5) since
and since the weighted sum of residuals is available as the running sum from Equation (1.3). The row and column for the satellite not used in the new term, represented by the current map, are then deleted from this covariance matrix and residual, to reduce the dimension by one. As explained, the reference map for the running sums is modified to replace the deleted satellite ID'"'"'s by “−1”, if the satellite has been missing for three or more Kalman cycles at 1 cycle per minute.
The covariance matrix is then inverted back to the information matrix with the satellites deleted. It is then uncompressed to an 8×8 by adding back in the rows and columns of zeroes. The running sum of weighted residuals with the satellites deleted can then be recomputed using Equation (1.4) to compute the right side from the left side:
The new terms can now be added to the running sums. If the new terms contain rows and columns for new satellites, these are added to the rows and columns of zeroes in those positions. The map with the new satellites replaces the reference map for the running sums.
As explained in the previous section, the terms in the sums in Equations (1.3) and (1.4) can be computed by separating the total group of terms into subgroups of several cycles each. The sums for each subgroup are computed first, and then the sums for each subgroup can be summed to determine the sum for the total group. There are two ways of doing this. The first is called “buffered averaging”. The second is called “sampled averaging”. These methods are described below.
In buffered averaging, the terms for each Kalman filter cycle are added to a running sum until a maximum number of cycles is reached. This maximum number of cycles is different for the navigation AIME™ Kalman filter than it is for the carrier loop AJAIME™ Kalman filter. For the navigation filter there are also different maximum numbers depending on the cycle rate, which in turn depends on the gyro grade used.
For civilian navigation grade gyros of 0.01 deg/hr, with Kalman update cycle rate of 1 cycle per minute, the maximum number accumulated is 10 representing ten minutes of averaging per buffer cycle. For the military 0.1 to 1.0 deg/hr grade gyro, the Kalman update cycle rate is 3 cycles/minute. The maximum number per buffer cycle is 6, representing two minutes of averaging. For the carrier loop AJAIME™ filter, whether civilian or military, the maximum number per buffer cycle is 10, representing ten seconds of averaging.
In any of the three cases, the accumulated totals are then stored in one position of a circular buffer with 12 buffer cycle positions. When 12 subgroup sums have been accumulated, the next subgroup sun replaces the first subgroup sum stored. Similarly, the next subgroup sum replaces the original second subgroup sum stored previously. In this way, the buffer stores the subgroup sums until 12 sums are accumulated and thereafter stores only the last 12 subgroup sums.
This process is repeated, with a pointer keeping track of the most recent position stored. This pointer counts from 1 to 12, when it is reset to 0, since 12=0 (MOD 12). In this way the buffer always stores the most recent 12 buffer cycles. When each new buffer subgroup sum is stored, the last 12 subgroup sums are summed over the past 12 buffer cycles according to Equations (1.3), and (1.4) where the k subscript now corresponds to each buffer cycle subgroup sum of 10 Kalman filter cycles at 1 cycle per minute or 6 cycles at 3 cycles per minute or 10 cycles at 1 cycle per second.
The estimated mean residuals and corresponding test statistic are computed from Equations (1.5) and (1.6) for the accumulated subgroup sums until the buffer is full and thereafter the estimated mean residuals and corresponding test statistic are computed for the past 12 buffer cycles. Similarly, the estimated mean residuals and corresponding test statistic are also or computed until 4 buffer cycles are accumulated and thereafter for the last 4 buffer cycles.
For the 0.01 deg/hr grade gyro navigation filter, when the buffer is full, it corresponds to 12×10=120 Kalman filter cycles. At one minute per cycle, this is a total averaging time of 120 minutes (two hours). The 4 cycle buffer average corresponds to a total averaging time of 40 minutes. The four cycle buffer average is intended to more quickly detect drifts which are not quite as slow as those detected by the 12 cycle buffer.
As an example, a drift of 0.02 m/s will cause a range error in that satellite of 144 meters after 120 minutes (7200 seconds). Such a drift is extremely rare, and is normally corrected by the Operational Control Segment (OCS). Even with selective availability (SA) on, this 144 meters is a 6sigma error, assuming a 24meter onesigma error from SA, after prefiltering for 1 minute. This 6sigma error would be detected with very high probability. The position error is less than HDOP×144 meters. Assuming a bad HDOP of 3, the maximum position error is only 432 meters, which is less than the 556 meter (0.3 nm) alert limit for nonprecision approach (NPA). With the lowpass filtering due to the long Kalman filter time constant, the position error will be reduced even further.
The test statistic for this buffer is only computed every ten minutes, assuming a navigation Kalman filter interval of 1 minute. Therefore, this test statistic is used only to detect the slowest satellite drifts of 0.3 m/s or less. A drift of 0.3 m/s causes a range error in the satellite of 180 meters in one buffer cycle subgroup of ten Kalman filter cycles (600 seconds). This drift will be detected in less than ten minutes since it is a 7.5 sigma error. Even with a bad HDOP of 3, this drift will be detected long before the position error approaches 540 meters (3×180 meters). The lowpass filter effect will reduce this maximum error to a fraction of 540 meters.
Faster drifts of 0.3 m/s or higher are detected by sampled averaging, which will now be described. In sampled averaging, there will be three levels of averaging.
The firstlevel quantities V_{1}, Vr_{1}, ΣV_{1}, and Σ Vr_{1 }are defined as follows:
(As explained in previous section on modifications required when satellites are changed: If Map V_{1}=Refmap_{1}, due to a new satellite which is new, changed, or missing—compress and decompress ΣV_{1}, ΣVr_{1 }to omit old satellite from slot for new satellites, and change Refmap_{1}=Map V_{1 }if satellite is missing for three or more cycles.)
The firstlevel quantities are initialized as follows:
The index k_{1 }is incremented and the quantities V_{1 }and Vr_{1 }are incrementally summed at each Kalman filter cycle until k_{1 }reaches a maximum count of k_{1max}:
When k_{1}=k_{1max}, the following operations are performed:
The secondlevel quantities are initialized as follows:
(As explained in the previous section on modifications required when satellites are changed: If Map V_{2}!=Refmap_{2}, due to a new satellite which is new, changed, or missing—compress and decompress ΣV_{2}, ΣVr_{2}, to omit old satellite from slot for new satellites, and change Refmap_{2}=MapV_{2}.)
The index k_{2 }is incremented and the quantities V_{2 }and Vr_{2 }are incrementally summed at each Kalman filter cycle until k_{2 }reaches a maximum count of k_{2max}:
ΣV_{2}=V_{2}+ΣV_{2}
When k_{2}=k_{2max}, the following operations are performed:
k_{2}=0, ΣV_{2}=0, and ΣVr_{2}=0.
The thirdlevel quantities are initialized as follows:
(If MapV_{3}!=Refmap_{3}, due to a new satellite which is new, changed, or missing, compress and decompress ΣV_{3}, ΣVr_{3}, to omit old satellite from slot for new satellites, and change Refmap_{3}=MapV_{3}.)
The index k_{3 }is incremented and the quantities V_{3 }and Vr_{3 }are incrementally summed at each Kalman filter cycle until k_{3 }reaches a maximum count of k_{3max}:
When k_{3}=k_{3max}, the following operations are performed:
As indicated below, the test statistic is computed as each new term is added after the first term in Equations (1.3) and (1.4). This is done to increase the likelihood of detection before the maximum number of cycles k_{1max }is accumulated. To indicate that the sums and test statistic for the next higher level are being accumulated, a subscript index “n” for the next higher level, followed by subscript “e” is used to indicate extrapolation.
The first level ssquare equations are:
The second level ssquare equations are:
The third level ssquare equations are:
When the number of cycles reaches k_{1max}, the counter k_{1 }is reset to zero, and the accumulated sums are stored as the first subgroup sum of the second level of averaging. The sums at the first level of averaging are then cleared to zero, and the first level averaging process is repeated.
The terms of subgroup sums are summed at each second level cycle whenever k_{1 }reaches k_{1max}. The second level counter k_{2 }is then incremented at each second level cycle until it reaches a count of k_{2max}. The test statistic is computed as each new subgroup sum is added, after the first term in Equations (1.3) and (1.4). This is done to increase the likelihood of detection before the maximum number of cycles k_{2max }is accumulated. When the number of cycles reaches k_{2max}, the counter k_{2 }is reset to zero, and the accumulated second level sums are stored as the first subgroup sum of the third level of averaging. The sums at the second level of averaging are then cleared to zero, and the second level averaging process is repeated.
The terms of second level subgroup sums are summed at each third level cycle whenever k_{2 }reaches k_{2max}. The third level counter k_{3 }is then incremented at each third level cycle until it reaches a count of k_{3max}, The test statistic is computed as each new third level subgroup sum after the first term is added in Equations (1.3) and (1.4). This is done to increase the likelihood of a detection before the maximum number of cycles k_{3max }is accumulated. When the number of cycles reaches k_{3max}, the counter k_{3 }is reset to zero, and the accumulated sums are stored as the final subgroup sum of the third level of averaging. The sums at the third level of averaging are then cleared to zero, and the third level averaging process is repeated
In the preferred embodiment, the maximum counts at each level are:
With these maximum counts, the test statistic is only computed once as the second new term is added after the first term in Equations (1.3) and (1.4). This means the first level will accumulate Kalman filter cycles for two minutes, the second level will accumulate two minute subgroups sums for four minutes, and the third level will accumulate four minute subgroup sums for 8 minutes.
The residuals are used to determine test statistics at each Kalman cycle, at each first level average every two cycles, at each second level average every 4 cycles, and at each third level average every 8 cycles. Since these averages are not computed in circular buffers, they are called sampled averages.
As an example for the navigation grade gyros, assuming SA, a satellite drift of 0.3 m/s will cause a range error in that satellite of 144 meters after 8 minutes (480 seconds). Since 144 meters is a 6sigma error, this drift will be detected in less than 8 minutes. The position error will be a very small fraction of 432 meters (3×144 meters) for HDOP of 3, because of the long Kalman filter time constant lowpass filter effect. Similarly, larger drifts will be detected sooner, with even smaller position errors.
Failures are detected by comparing the s^{2 }statistics with thresholds. When there are no failures in the Kalman filter from which s^{2}was computed, the statistic s^{2 }is central Chisquare distributed. The detection threshold s_{D}^{2 }is selected at the tail of the distribution, to result in a low specified false alarm rate when there are no failures.
The statistics computed at 1 Kalman filter cycle, at the sampled averaging sample times, and at the buffered averaging cycle lengths, are shown below.
In general, fast failures, such as large step changes or large ramp errors in the satellites, are first detected by the statistics with shortest averaging time. Slow failures are detected by the statistics with the longest averaging times.
In addition to the Kalman filter f_{0}, which is modeled for normal errors with no failures, there is a bank of ten Kalman filters running in parallel, each modeled for a different subsystem failure. These Kalman filters are listed below.
The first filter, f_{0 }is used to detect, but not isolate, a failure condition in any one of the subsystems. The other filters are used to isolate the failure to a specific subsystem, once a failure condition is detected. In addition to computing the position outputs, each test filter will also compute the twosigma accuracy figure of merit (FOM), and the horizontal integrity level (HIL), based on its own Kalman filter tuning. When the failure is isolated to a particular subsystem, it is safe to use the outputs from the corresponding test filter, since it is insensitive to that subsystem failure. The outputs from this filter are therefore selected by index fx.
The isolation is achieved by computing statistics as above for each of the Kalman filters in the above table. When a failure occurs, only one of the test filters will continue to have small, uncorrelated residuals, and correspondingly small test statistics. This is the filter which is tuned for that specific subsystem failure. The failure is assumed to have occurred in the subsystem with the smallest test filter statistics at the maximum averaging times where the corresponding detection threshold is exceeded. Since this filter is insensitive to failures in that subsystem, it is safe to use the position solution from this test filter.
The P'"'"'s in the description column of the above table are the covariances that the Kalman filter is initialized with. These are generally diagonal terms corresponding to the manufacturer'"'"'s specifications on the error variance. The only offdiagonal terms are those due to correlations of some of the inertial error states at the end of a short alignment since the Kalman filter is initialized at the end of a short alignment.
For the satellites, the diagonal element corresponding to that satellite range bias error state is initialized or reinitialized when the satellite first comes into view. All other elements in the corresponding row and column are initialized with zeroes, since the satellite error is initially uncorrelated with any of the other error states.
For satellite failure test filters f_{1 }through f_{8}, the principal change in the Kalman filter model is to increase the process noise Q for that satellite range bias error state, or carrier phase error, until that satellite measurement is effectively ignored in the updates for that test filter.
For the IMUBaro failure test filter f_{9}, the process noises Q for all gyro, accelerometer, and baro altitude error states is increased, but not so much that the ADIRS is assumed to be completely unusable. Instead, the Kalman filter f_{9 }gives more weight than normal to the GPS measurements, so that it relies less on IMUBaro coasting capability. It therefore only models soft failures, since hard failures will shut the system down completely.
When there is a soft IMUBaro failure, such as a large change in gyro bias error, or a rapid baroinertial altitude bias change due to a weather front, the residuals and test statistics from this filter will remain small. The test statistics from all of the other filters will be large. A soft failure is therefore assumed to have occurred in the IMUBaro system. In this case, it is safe to use the position output from this test filter, since it is relatively insensitive to such changes. This is accomplished by using the index, fx, which specifies which filter outputs to use.
When there are large errors in more than one satellite, such as due to the onset of jamming or spoofing, or due to GPS receiver problems, the residuals and test statistics from the GPS failure test filter will remain small. The test statistics from all of the other filters will be large. In this case, it is safe to use the position output from this test filter, since it relies mostly on IRS coasting with only gradual updating from the GPS. The index fx then specified this filter for the outputs. The outputs from this filter can also be used when there is more than one satellite failure at a time or when the statistics from more than one satellite failure test filter are small due to unusual geometry, making it difficult to isolate the bad satellite.
When there is a failure, and it is isolated to a bad subsystem, the failure condition is remodeled in the other test filters, but not to the nofailure test filter f_{0}. This is done so that these other filters will eventually recover from the failure, even though they are contaminated at the time of detection and isolation, as evidenced by their residuals and test statistics being large.
For satellite failures, the failure condition is modeled in all of the other test filters by deleting the failed satellite from the measurements. If the original satellite was excluded incorrectly, the test statistic of the nofailure filter may again give small residuals. If its statistic is less than one half the threshold, the output will again be taken from the nofailure filter, and the previously excluded satellite will again be used by all filters.
Another possibility is that the test statistic for one of the other test filters will become smaller than that for the original test filter, which incorrectly excluded the wrong satellite. This could also occur if two satellites fail at nearly the same time, although this is extremely unlikely. If this occurs, the second satellite is also excluded from all of the other test filters, and the outputs from the second satellite test filter are safe to use.
If the first satellite was incorrectly excluded, its test statistic will again exceed the threshold, and it will again be accepted as a new satellite by the other test filters, which have been excluding both satellites. If the nofailure filter statistic becomes less than onehalf the detection threshold, all excluded satellites will be used by all of he test filters.
The failure condition for failure of the IMUBaro is remodeled in the other filters by increasing the Q for one of the instruments. The failed instrument is selected by checking which instrument, or instruments, have estimated error state variables which exceed a predetermined threesigma limit in the ADIRS failure test filter (or nofailure filter). The state variables checked are the barooffset, each gyro, and each accelerometer.
The Q selected for increase is thereafter decreased exponentially with a 20 minute time constant in all filters, except in the [MUBaro failure test filter where all Q'"'"'s remain large. If the nofailure residuals and test statistic of the nofailure filter eventually become smaller than a two sigma threshold, the outputs are again taken from the nofailure filter rather than from the IMUBaro failure test filter. This is done because the nofailure filter has only one large Q, which is decaying exponentially, while all the gyro, accelerometer, and baro Q'"'"'s of the IMUBaro failure test filter remain large.
FIG. 1 shows how AJAIME™ closes the carrier and code tracking loops. The Carrier NCO and CODE NCO_{i }boxes and the boxes above these boxes in FIG. 1 show the conventional mechanization of a carrier Costas loop and a code delay lock loop except for the conventional filters which close the loop.
The boxes below the Carrier NCO and CODE NCO_{I }boxes show a Doppler phase rotation increment command φ(t+t_{p}) entering the Carrier NCO box. In a conventional Costas loop, this command is the output of a first, second, or third order filter. In FIG. 1 it is the 1000Hz output of the Extrapolator with small 1000Hz aiding corrections from the AJAIME KF (Kalman Filter) and State Error Extrapolator.
The Carrier Phase Error (I_{p}Q_{p}=e_{φ}) coming from the discriminator, which would go into the Costas loop filter in a conventional mechanization, enters the AJAIME KF box. In effect, the Extrapolator and the AJAIME KF and State Error Extrapolator replace the Costas loop filter that conventionally closes the carrier tracking loop.
Who The AJAIME mechanization is really a sequence of three filters in series, each aiding the next. The AIME KF (Kalman Filter) has the slowest update rate. The rate is 1 cycle per minute, for 0.01 deg/hr navigation grade gyros, and the rate is 3 cycles per minute for missile applications with lower grade gyros of 0.1 deg/hr to 1.0 deg/hr. The measurements to this filter are the PR measurements at 1 Hz, averaged or prefiltered over the update interval of one to three cycles per minute. Further details concerning the AIME KF can be found in U.S. Pat. No. 5,583,774 which is incorporated by reference.
The AIME KF uses PR measurements rather than phase measurements, since IMU errors such as gyro and accelerometer bias errors or reference navigation axis alignment errors, take many minutes to estimate. The error states and correlation times are shown below for both navigation grade gyros, and for lower grade gyros which might be used in missiles or munitions.
The correlation times of these navigation error sources are from ten minutes to an hour. It is best to use a measurement such as PR, which has a bounded position error over these long correlation periods. Without DGPS, the integrated carrier Doppler phase error becomes unbounded over long periods of time.
Update Rate 1/min., navigation grade gyros; 3/min, AHRS grade gyros.
Process Nose: Q_{n}=2σ^{2}_{n}(1/τ_{n})
The corresponding dynamics matrix is shown below.
The measurements and observation matrices are shown below.
Enroute through nonprecision approx. (NPA): dt_{k}=1 minute (60 s)
Precision Approach (PA) or missiles: dt_{k}=⅓ minute (20 s)
The error state corrections x(t) are sent to the NAV computer in FIG. 1. This is done at a rate of 1 Hz, by using transition matrix extrapolation between update intervals of ⅓ to 1 minute. The NAV computer adds these slowlyvarying 1Hz corrections to the uncalibrated navigation solution based on uncalibrated IMU inputs. This is done at the navigation solution rate of 200 Hz to obtain the calibrated position output P(t_{k}) which is sent to the AJAIME KF and State Error Extrapolator.
The inertial errors, lowfrequency user clock errors, and lowfrequency atmospheric errors have already been removed by the AIME KF. The AJAIME KF and State Error Extrapolator only has to estimate small, residual, high frequency errors in the signal to each satellite. These errors are the high frequency clock errors, the high frequency atmospheric anomalies and multipath errors, and the gravity anomalies. These errors each have correlation times of less than 5 minutes as shown below. As shown, the correlation time of the gravity anomaly errors is proportional to ground speed (V_{GS}), since the gravity anomalies vary with distance. The correlation distance is taken here to be 10 nm, rather than the usual 20 nm, since a firstorder Markov model is used, rather than the secondorder model of Gelb.
Update Rate: 1 Hz
Process Noise: Q_{n}=2σ^{2}_{n }(1/τ_{n})
The corresponding dynamics matrix is shown below.
The corresponding measurement and observation matrices are shown below.
Update interval dt_{k}=1 second; dt_{m}=0.020 s (20 ms)
Transition matrix from t_{k }to t_{m}: Φ(t_{m}−t_{k})
It is assumed here that the body mounted IMU axes lie along the principal (eigenvector) axes of the moment of inertia tensor of the aircraft or missile, and that the autopilot controls the angular rates, ω_{x}, ω_{y}, ω_{z }about these axes. The angular rates and angular accelerations can then be predicted a short time t_{p }into the future at 1000 Hz by the body axis extrapolator shown in FIG. 2. This prediction time is necessary because of the time lag in rotating the carrier phase in the Carrier NCO shown in FIG. 1. If the phase is rotated at constant rate every 5 milliseconds, the prediction time must be at least 5 milliseconds. If the phase is rotated at a changing rate every millisecond, the prediction time must be at least 1 millisecond. The sampling switch at interval T_{k }shown in FIG. 2 is also necessary when IMU measurements are asynchronous with the Carrier NCO.
It is also assumed that the autopilot controls the acceleration along body axes, by controlling lift and thrust forces. The linear accelerations are also predicted as shown by the dV Predictor in FIG. 2.
The rate of change of angular acceleration, called angular “jerk”, is not estimated by the body axis extrapolator of FIG. 2. Using as an example a 1foot lever arm from the center of mass to the GPS antenna, a maximum antenna jerk of 10 g'"'"'s/sec would result from an angular jerk of 320 radian/sec^{2 }per second. This would be typical of a low performance aircraft or missile. A maximum jerk of 100 g'"'"'s per second, resulting from 3200 rad/sec^{2 }per sec, would be typical for high performance aircraft or missiles. A jerk of 1000 g'"'"'s per second, resulting from 32,000 radians/sec^{2 }per second, would be typical for many munitions.
With 1foot lever arm, the 1000 g/sec jerk resulting from 32,000 rad/sec^{3 }is the same as 32,000 feet/sec^{3}. This is approximately 10,000 meters/sec^{3}, which is 0.010 millimeter/millisecond^{3}. The maximum phase displacement with prediction time 5 milliseconds is 0.2083333 mm, obtained from (0.010 mm/ms^{3})×(5ms)^{3}/6.
Similar results apply to the linear acceleration predictor in FIG. 2. Although 1000 g'"'"'s/sec seems quite large, it corresponds to changing the acceleration by 10 g'"'"'s in 0.01 seconds, or by 100 g'"'"'s in 0.1 second. Angular acceleration jerks about the roll axis are sometimes larger than linear acceleration jerks. Larger jerks can be accommodated by increasing the angular measurement rate from the IMU from 400 Hz to 1600 Hz, and by reducing the constant phase rate rotation interval in the Carrier NCO to 1 millisecond.
Since the phase angle and velocity increments in FIG. 2 are in body axes, they must be converted to position along the lineofsight (LOS) in order to rotate the phase of the Carrier NCO. This is done by the algorithm shown below.
Define:
dt_{k}=5 ms (200 Hz)
BodytoReference Direction Cosine (DC) Matrix
Body Axes:
Reference (NAV) axes:
1000 Hz:
The first step is to determine the direction cosine (DC) matrix from body (B) to navigation reference (R) axes at the predicted time t+t_{p}. This is accomplished by accumulating angular increments along body axes from the last navigation solution at time t_{k }to the predicted time t+t_{p}. Using small angle approximations, the DC matrix at the last navigation solution time t_{k }is then rotated through the accumulated angle Δφ^{B }to obtain an approximate DC matrix [C_{B}^{R}(t+t_{p})] at 1000 Hz. This matrix is then used to convert the last dV^{B }increment at time t+t_{p }to navigation reference axis increment dV^{R}(t+t_{p}) at 1000 Hz. These increments along navigation reference axes are accumulated to obtain delta position of the IMU from last navigation solution time t_{k }to predicted time t+t_{p}.
By adding these positions, the total position of the IMU is obtained. It remains to add the relative position of the GPS antenna due to the lever arm vector R_{L}^{B}] from the IMU to the GPS antenna. Although R_{L}^{B}] is fixed in body axes, it must be resolved along navigation reference axes, as was d_{V}^{B}]. The resulting position is then resolved along the LOS to each satellite, using the slowly varying satellite LOS direction cosines e_{i}] relative to the navigation reference axes. These direction cosines are already computed from the navigation filter at 1 Hz.
Because of the aiding from the slow (3 per minute) AIME™ KF to the fast (1 Hz) AJAIME KF and the aiding from this fast Kalman filter (1 Hz) to the Extrapolator (1000 Hz), positions computed at different rates are being added. In order to provide continuous 1000 Hz phase, it is necessary to lowpass filter each aiding position and to sample at a much higher rate before adding to the faster filter at each level.
Since only relative change in phase is needed, the positions are periodically reduced by an No amount corresponding to the change since the last slow computer time t_{k }to time t+t_{p}, and only the position change is resolved along the LOS to add to the phase at the last computer time t_{k}. Whenever the position is reduced, the low pass filter for that position is reduced by the same amount, in order that the filter output is continuous. In addition, the phase is reduced by the phase at the last slow computer time t_{k}. Since the phase is computed in fractions of a revolution, modulus1 revolution, the change in phase is computed correctly, even though the total accumulated phase never exceeds one revolution.