High performance dual mode mixing inertial navigation system

Patent metadata

Field	Value
Publication number	US-11280617-B2
Application number	US-201916550407-A
Country	US
Kind code	B2
Filing date	Aug 26, 2019
Priority date	Aug 26, 2019
Publication date	Mar 22, 2022
Grant date	Mar 22, 2022

Abstract

The dual mode INS high performance system using an IMM mixing method presented in this disclosure utilizes a MEMS-based IMU as the inertial sensor due, in part, to low cost and low power consumption. A dual filtering system, one with fifteen state EKF and the other with twenty one state EKF is mixed in real-time to address dynamic overlapping and continuous transition from a low dynamic mode to a high dynamic mode. The IMM estimation scheme is employed to compute the real-time mixing coefficients via the IMM probability mixing formulation. The present disclosure is applicable to earth to orbit platforms, spinning projectiles, and the like to successfully reconstruct the object's state estimate vector even under a high angular rate and high g operating environment.

First claim

What is claimed:

1. An inertial navigation system, comprising:
an inertial measurement unit (IMU) wherein the inertial measuring unit provides a platform's specific force and angular rate measurements;
one or more sensors configured to provide information to aid in an estimate of the error sources for a subsequent calibration and removal of the errors at the inertial measuring unit measurement level; and
a processor, the processor configured to:
calculate a state and a covariance for a plurality of Extended Kalman Filter (EKF) models, wherein a first EKF model is a fifteen state EKF and a second EKF model is a twenty one state EKF;
update, over time, the state and the covariance for the first and the second EKF models, respectively;
estimate and remove high bias drift errors and scale factor errors from the inertial measuring unit data; and
provide a solution having an error of less than 30 feet for a variety of platforms.

2. The system according to claim 1 , wherein mixing is done in real time using an interacting multiple model (IMM) estimation and processing scheme.

3. The system according to claim 2 , wherein mixing is based on mixing coefficients calculated using an interacting multiple model mixing scheme.

4. The system according to claim 3 , wherein the interacting multiple model mixing scheme captures the continuous dynamic mode transition from a nominal to a high dynamic operating condition.

5. The system according to claim 1 , wherein one of the one or more external aiding sensors is a radio frequency orthogonal interferometry enabled sensor.

6. The system according to claim 1 , wherein the inertial measuring unit and the one or more external aiding sensors are co-located on a vehicle or a projectile.

7. The system according to claim 1 , wherein the fifteen state Extended Kalman Filter is used for a low dynamic mode operating condition using bias errors for the inertial measuring unit and platform state error estimations for position, velocity, and attitude.

8. The system according to claim 1 , wherein the twenty one state Extended Kalman Filter is used for a high dynamic mode operating condition using bias errors and scale factor errors for the inertial measuring unit and platform state error estimations for position, velocity, and attitude.

9. The system according to claim 1 , wherein multiple external aiding sensor data is mixed using a multiple model estimation scheme wherein each external aiding sensor can be assigned to each individual Extended Kalman Filter (EKF), and the multiple external aiding sensors are then processed using an interacting multiple model (IMM) at the individual EKF state level.

10. A method of producing a high performance inertial navigation solution using a MEMS based inertial measuring unit, comprising:
providing an inertial measuring unit, wherein the inertial measuring unit provides a platform's specific force and angular rate measurements;
providing one or more external aiding sensors, wherein the one or more external aiding sensors provide information to aid in an estimate of the error sources for a subsequent calibration and removal of the errors at the inertial measuring unit measurement level;
calculating a state and a covariance for a plurality of Extended Kalman Filter (EKF) models, wherein a first EKF model is a fifteen state EKF and a second EKF model is a twenty one state EKF;
updating over time the state and the covariance for the first and the second EKF models, respectively; and
estimating and removing high bias drift errors and scale factor errors from the inertial measuring unit data;
thereby providing a solution having an error of less than 30 feet for a variety of aerospace platforms and/or projectiles.

11. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein mixing is done in real time using an interacting multiple model (IMM) estimation and processing scheme.

12. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 11 , wherein mixing is based on mixing coefficients calculated using an interacting multiple model mixing scheme.

13. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 12 , wherein the interacting multiple model mixing scheme captures the continuous dynamic mode transition from a nominal to a high dynamic operating condition.

14. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein one of the one or more external aiding sensors is a radio frequency orthogonal interferometry enabled sensor.

15. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein the inertial measuring unit and the one or more external aiding sensors are co-located on a vehicle or a projectile.

16. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein the fifteen state Extended Kalman Filter is used for a low dynamic mode operating condition using bias errors for the inertial measuring unit and platform state error estimations for position, velocity, and attitude.

17. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein the twenty one state Extended Kalman Filter is used for a high dynamic mode operating condition using bias errors and scale factor errors for the inertial measuring unit and platform state error estimations for position, velocity, and attitude.

18. The method of a dual mode high performance inertial navigation solution using a MEMS based inertial measuring unit according to claim 10 , wherein multiple external aiding sensor data is mixed using a multiple model estimation scheme wherein each external aiding sensor can be assigned to each individual Extended Kalman Filter (EKF), and the multiple external aiding sensors are then processed using an interacting multiple model (IMM) at the individual EKF state level.

Assignees

• Bae Sys Inf & Elect Sys Integ

Inventors

• Lam Quang M

Classifications

• G01C21/165 Primary

  combined with non-inertial navigation instruments · CPC title