Miniaturized GPS/MEMS IMU integrated board

• US 8,275,193 B2
• Filed: 01/08/2008
• Issued: 09/25/2012
• Est. Priority Date: 08/04/2004
• Status: Active Grant

• Current Assignee: American GNC Corporation
• Original Assignee: American GNC Corporation
• Inventors: Lin, Ching-Fang
• Primary Examiner(s): Wang, Claire X
• Assistant Examiner(s): Allison, Andrae S
• Application Number:
  US12/008,205
• Publication Number:
  US 20100283832A1
• Time in Patent Office: 1,722 Days
• Field of Search: 382/103, 382/106, 382/154, 382/156, 382/168, 382/171, 382/173, 382/181, 382/262, 356/12, 701/468
• US Class Current: 382/154
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• CPC Class Codes: G06T 2207/20164 Salient point detection; Corner detection
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/74 involving reference images or patches
• CPC Class Codes: G06V 10/245 by locating a pattern; Special marks for positioning
• CPC Class Codes: G06V 10/811 the classifiers operating on different input data, e.g. multi-modal recognition

Abstract

This invention documents the efforts on the research and development of a miniaturized GPS/MEMS IMU integrated navigation system. A miniaturized GPS/MEMS IMU integrated navigation system is presented; Laser Dynamic Range Imager (LDRI) based alignment algorithm for space applications is discussed. Two navigation cameras are also included to measure the range and range rate which can be integrated into the GPS/MEMS IMU system to enhance the navigation solution.

Claims

1. A method of three dimensional positioning of objects, comprising the steps of:

(a) injecting raw measurements from two or more sensors into a data fusion system to derive relative position and attitude information of objects;

(b) tracking at least four common GPS (Global Positioning System) satellites simultaneously by means of AERCam (autonomous Extravehicular Activity Robotic Camera) 1′ and

ISS (International Space Station) 2′

;

(c) optimally blending multipath signal and GPS signal due to any intermittently fail of relative GPS-alone when said AERCam 1′

maneuvers into a proximity of said ISS 2′

because of occasional strong multipath signal and GPS signal blockage with IMU data including gyro and accelerometer measurements;

(d) initializing an inertial navigation system when a GPS solution is available and using said GPS solution;

(e) tracking position and attitude of said AERCam 1′

relative to said ISS 2′

by said inertial navigation system in close proximity;

(f) estimating and resetting frequently for errors of said inertial navigation system growing with time;

(g) aiding said inertial navigation system during close proximity operation with available GPS signals and at least two cameras installed on said AERCam 1′

to maintain accurate relative navigation, wherein range and range rate between said AERCam 1′ and

said ISS 2′

are blended in multi-sensor fusion algorithm to compensate for IMU sensor drifts during GPS outage or unreliable GPS data; and

(h) imaging said ISS 2′

by at least two cameras installed on said AERCam 1′

for images which are further processed via correlation and coordinate transformations techniques to derive range and range rate on-board of said ACRCam 1′

for image parameters which are able to be computed and transmitted said range and range rate back to said AERCam 1′

.

2. The method, as recited in claim 1, wherein, in the step (c), three accelerometers and three gyros are used in a multi-sensor based navigation system to provide three-dimensional position and attitude solutions.

3. The method, as recited in claim 1, further comprising a step of performing intelligent failure detection and isolation for multiple disparate navigation sensors by using one or more neural networks and generate filtered sensor estimates for said sensors even though some of the sensor measurements have been corrupted by noise, disturbances or failures.

4. The method, as recited in claim 3, wherein said neural network has four hidden layers for information compression and data regeneration and is trained to generate said filtered sensor estimates.

5. The method, as recited in claim 4, further comprising a step of determining continuously coordinates with respect to an unknown point by means of a MEMS coremicro IMU supporting said AERCam 1′

.

6. The method, as recited in claim 5, further comprising a step of obtaining accurate positioning with said GPS by using of carrier phase observables.

7. The method, as recited in claim 6, further comprising a step of mitigating a potential impact on accuracy of said MEMS coremicro IMU via smart integration algorithms with navigation sensors, including rate sensors and accelerometers, wherein sources of said rate sensors and accelerometers have been sought out and characteristics of said rate sensors and accelerometers are obtained so that models of said MEMS coremicro IMU are able to be generated for simulations of said AERCam 1′

, including sensor biases, drift rates, response times, and noise sources.

8. The method, as recited in claim 1, further comprising a step of aiding acquisition and tracking of GPS signals by said inertial navigation system to extend dynamics thereof while maintaining a minimum tracking loop bandwidth.

9. The method, as recited in claim 8, further comprising a step of providing mechanics of using GPS solution to aid said inertial navigation system and using inertial navigation system solution thereof to aid code and carrier tracking of a GPS receiver.

10. The method, as recited in claim 9, wherein said GPS receiver provides raw pseudorange and carrier phase observables for an integrated navigation Kalman filter and a synchronous timing signal for date sampling electronics of said MEMS coremicro IMU, wherein IMU instrument errors are corrected in said integrated navigation Kalman filter and said IMU electronics.

11. The method, as recited in claim 10, wherein said GPS receiver accomplishing identification of all visible satellites with calculation of geometric dilution of precision, measurement of satellite-to-receiver pseudoranges and decoding of navigation messages, delivery of data to a navigation microprocessor, and receiving of velocity and acceleration aiding data from said integrated navigation Kalman filter to perform external aiding, carrier phase and code tracking.

12. The method, as recited in claim 11, wherein an absolute AERCam 1′

trajectory is not needed for navigation algorithm development and validation while a precision relative trajectory with respect to said ISS 2′

for navigation algorithm development is needed, wherein said trajectory of said AERCam 1′

is generated as a small perturbation of said ISS 2′

trajectory.

13. The method, as recited in claim 12, wherein a sophisticated trajectory generation is able to use Chohessy-Wiltshire formulation to include dynamics of said AERCam 1′

, said trajectory of said ISS 2′

is able to be obtained from orbital ISS 2′

model and measurements.

14. The method, as recited in claim 13, wherein said AERCam 1′

is a small free-flying unmanned platform capable of teleoperation and autonomous flight in close proximity to said ISS 2′

, which is able to hold position relative to fly trajectories about a target thereof, wherein said trajectory of said AERCam 1′

in an Earth-Centered-Earth-Fixed (ECEF) coordinate system is related to said trajectory of said ISS 2′

, wherein a relative trajectory of said AERCam 1′

with respect to said ISS 2′

is a low-dynamics process with low-speed motion and slow change in relative position.

15. The method, as recited in claim 14, further comprising the steps of:

generating a standard six-degree-of-freedom (6-DOF) trajectory in said ECEF coordinate system for said ISS 2′

;

generating said relative trajectory with respect to said ISS 2′

for said AERCam 1′

; and

merging said trajectory of said ISS 2′

in said ECEF coordinate system and said relative trajectory of said AERCam 1′

to obtain said trajectory of said AERCam 1′

in said ECEF coordinate system.

16. The method, as recited in claim 15, further comprising a step of transforming trajectory data in said ECEF coordinate system into an Earth Centered Inertial coordinate system to meet HIL simulation requirements.

17. The method, as recited in claim 15, wherein said relative trajectory of said AERCam 1′

is used for evaluation relative navigation solution and developing collision avoidance algorithm and said trajectory of said AERCam 1′

in said inertial navigation system is used for navigation sensor validation and simulation, including said GPS receiver and said MEMS coremicro IMU.