Methods for attitude and heading reference system to mitigate vehicle acceleration effects

• US 9,709,405 B2
• Filed: 11/23/2015
• Issued: 07/18/2017
• Est. Priority Date: 11/23/2015
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Sotak, Milos, Malinak, Pavol, Kana, Zdenek, Dunik, Jindrich
• Primary Examiner(s): Oritz, Angela
• Assistant Examiner(s): BERNS, MICHAEL ANDREW
• Application Number:
  US14/949,437
• Publication Number:
  US 20170146347A1
• Time in Patent Office: 603 Days
• CPC Class Codes: B64D 43/02 for indicating aircraft speed or stalling conditions
• CPC Class Codes: G01C 19/00 Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
• CPC Class Codes: G01C 21/18 Stabilised platforms, e.g. by gyroscope
• CPC Class Codes: G01C 21/188 for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
• CPC Class Codes: G01C 23/00 Combined instruments indicating more than one navigational value, e.g. for aircraft; Combined measuring devices for measuring two or more variables of movement, e.g. distance, speed or acceleration
• CPC Class Codes: G01P 15/18 in two or more dimensions

Abstract

An attitude and heading reference system (AHRS) for a vehicle is provided along with methods for mitigating vehicle acceleration effects in the AHRS. The AHRS comprises an inertial measurement unit configured to generate inertial measurements from at least one accelerometer and at least one gyroscope, a heading source configured to generate heading measurements, and an adaptive statistical filter configured to mitigate the impact of vehicle acceleration effects on attitude and heading.

Claims

1. An attitude and heading reference system (AHRS) for a vehicle, the AHRS comprising:

an inertial measurement unit (IMU) onboard the vehicle and configured to generate inertial measurements comprising angular rate and acceleration measurements of the vehicle;

a heading source onboard the vehicle and configured to generate heading measurements of the vehicle; and

a processor including an adaptive statistical filter onboard the vehicle and in operative communication with the IMU and the heading source, wherein the processor utilizes the adaptive statistical filter to mitigate the impact of vehicle acceleration effects on attitude and heading during operation of the vehicle, wherein the processor is configured to;

estimate an attitude and heading of the vehicle;

model and estimate IMU errors and heading source errors; and

model, adapt, and estimate vehicle acceleration using an adaptation algorithm configured to;

detect acceleration of the vehicle;

compute a synthetic acceleration;

compute a vehicle acceleration upper boundary; and

transform the vehicle acceleration upper boundary onto parameters for the modeled vehicle acceleration; and

mitigate the impact of vehicle acceleration effects on the attitude and heading estimate during operation of the vehicle using the vehicle acceleration upper boundary.

2. The AHRS of claim 1, wherein the adaptive statistical filter comprises an algorithm providing a probabilistic description of estimated quantities.

3. The AHRS of claim 2, wherein the adaptive statistical filter comprises an extended Kalman filter.

4. The AHRS of claim 2, further comprising an upper boundary computation module that includes a first vehicle acceleration adaptation algorithm configured to:

provide a vehicle acceleration model based on measured variables and estimated variables;

compute a vehicle acceleration upper boundary (α

) based on a synthetic acceleration (^B) as;

α

=∥

^B∥

≈

∥

{tilde over (f)}^B+Ĉ

_N^Bĝ

^N−

{circumflex over (b)}_accB^B∥

where {tilde over (f)}^B is a measured specific force signal output from the accelerometer, Ĉ

_N^Bĝ

^N is the estimated gravity vector signal, and {circumflex over (b)}_accB^B is the estimated accelerometer bias signal;

compute a steady-state standard deviation (σ

_{steadyState,GMacceleration}) of a vehicle acceleration first-order Gauss-Markov (GM) process as;

σ

_{steadyState,GMacceleration}²=(η

σ

_{steadyState,accelerometer})²+α

²

where η

is an overbounding factor;

compute a power spectral density of the vehicle acceleration GM process driving noise as;

5. The AHRS of claim 2, further comprising an upper boundary computation module that includes a second vehicle acceleration adaptation algorithm configured to:

provide a vehicle acceleration model based on measured variables and estimated variables;

compute a vehicle acceleration upper boundary (α

) based on a synthetic acceleration ({tilde over (â

)}^B) as;

6. The AHRS of claim 1, wherein the IMU comprises a three-axis accelerometer and a three-axis gyroscope.

7. The AHRS of claim 1, wherein the vehicle is an aircraft.

8. The AHRS of claim 1, wherein the heading source comprises a magnetometer.

9. A method for mitigating vehicle acceleration effects in an attitude and heading reference system (AHRS), the method comprising:

obtaining inertial data from an inertial measurement unit (IMU) onboard a vehicle, the inertial data comprising angular rate and acceleration measurements of the vehicle;

obtaining heading measurements of the vehicle from a heading source onboard the vehicle; and

performing computations utilizing an adaptive statistical filter in a processor onboard the vehicle, the processor in operative communication with the IMU and the heading source, the computations comprising;

estimating an attitude and heading of the vehicle;

modeling and estimating IMU errors and heading source errors;

modeling and estimating vehicle acceleration using an adaptation algorithm comprising;

detecting acceleration of the vehicle;

computing a synthetic acceleration;

computing a vehicle acceleration upper boundary; and

transforming the vehicle acceleration upper boundary onto parameters for the modeled vehicle acceleration; and

mitigating the impact of vehicle acceleration effects on the attitude and heading estimate during operation of the vehicle using the vehicle acceleration upper boundary.

10. The method of claim 9, wherein the adaptive statistical filter comprises an algorithm providing a probabilistic description of estimated quantities.

11. The method of claim 10, wherein the upper boundary computation is performed by a first vehicle acceleration adaptation algorithm comprising:

providing a vehicle acceleration model based on measured variables and estimated variables;

computing a vehicle acceleration upper boundary (α

) based on a synthetic acceleration (^B) as;

α

=∥

^B∥

≈

∥

{tilde over (f)}^B+Ĉ

_N^Bĝ

^N−

{circumflex over (b)}_accB^B∥

where {tilde over (f)}^B is a measured specific force signal output from the accelerometer, Ĉ

_N^Bĝ

^N is an estimated gravity vector signal, and {circumflex over (b)}_accB^B is an estimated accelerometer bias signal;

computing a steady-state standard deviation (σ

_{steadyState,GMacceleration}) of a vehicle acceleration first-order Gauss-Markov (GM) process as;

σ

_{steadyState,GMacceleration}²=(η

σ

_{steadyState,accelerometer})²+α

²

where η

is an overbounding factor;

computing a power spectral density of the vehicle acceleration GM process driving noise as;

12. The method of claim 10, wherein the upper boundary computation is performed by a second vehicle acceleration adaptation algorithm comprising:

providing a vehicle acceleration model based on measured variables and estimated variables;

computing a vehicle acceleration upper boundary (α

) based on a synthetic acceleration (^B) as;

13. The method of claim 9, wherein the adaptive statistical filter comprises an extended Kalman filter.

14. The method of claim 9, wherein the IMU comprises a three-axis accelerometer and a three-axis gyroscope.

15. The method of claim 9, wherein the vehicle is an aircraft.

16. The method of claim 9, wherein the heading source comprises a magnetometer.

17. A computer program product comprising:

a non-transitory computer readable medium having instructions stored thereon executable by a processer to perform a method for mitigating vehicle acceleration effects in an attitude and heading reference system (AHRS), the method comprising;

obtaining inertial data from an inertial measurement unit (IMU) onboard a vehicle, the inertial data comprising angular rate and acceleration measurements of the vehicle;

obtaining heading measurements of the vehicle from a heading source onboard the vehicle; and

performing computations utilizing an adaptive statistical filter in a processor onboard the vehicle, the processor in operative communication with the IMU and the heading source, the computations comprising;

estimating an attitude and heading of the vehicle;

modeling and estimating IMU errors and heading source errors;

modeling and estimating vehicle acceleration using an adaptation algorithm comprising;

detecting acceleration of the vehicle;

computing a synthetic acceleration;

computing a vehicle acceleration upper boundary; and

transforming the vehicle acceleration upper boundary onto parameters for the modeled vehicle acceleration; and

mitigating the impact of vehicle acceleration effects on the attitude and heading estimate during operation of the vehicle using the vehicle acceleration upper boundary.

18. The computer program product of claim 17, wherein the upper boundary computation is performed by a first algorithm that generates the vehicle acceleration mitigation data, the first algorithm comprising:

providing a vehicle acceleration model based on measured variables and estimated variables;

computing a vehicle acceleration upper boundary (α

) based on a synthetic acceleration (^B) as;

α

=∥

^B∥

≈

∥

{tilde over (f)}^B+Ĉ

_N^Bĝ

^N−

{circumflex over (b)}_accB^B∥

where {tilde over (f)}^B is a measured specific force signal output from the accelerometer, Ĉ

_N^Bĝ

^N is an estimated gravity vector signal, and {circumflex over (b)}_accB^B is an estimated accelerometer bias signal;

computing a steady-state standard deviation (σ

_{steadyState,GMacceleration})) of a vehicle acceleration first-order Gauss-Markov (GM) process as;

σ

_{steadyState,GMacceleration}²=(η

σ

_{steadyState,accelerometer})²+α

²

where η

is an overbounding factor;

computing a power spectral density of the vehicle acceleration GM process driving noise as;

19. The computer program product of claim 17, wherein the upper boundary computation is performed by a second algorithm that generates the vehicle acceleration mitigation data, the second algorithm comprising:

providing a vehicle acceleration model based on measured variables and estimated variables;

computing a vehicle acceleration upper boundary (α

) based on a synthetic acceleration (^B) as;