Adaptive gps and ins integration system

• US 20040012522A1
• Filed: 12/09/2002
• Published: 01/22/2004
• Est. Priority Date: 06/07/2000
• Status: Active Grant

• Current Assignee: Qinetiq Limited (QinetiQ Group PLC)
• Original Assignee: Qinetiq Limited (QinetiQ Group PLC)
• Inventors: Groves, Paul D
• Granted Patent: US 6,900,760 B2
• Time in Patent Office: Days
• US Class Current: 342/357.14
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01S 19/26 involving a sensor measurement for aiding acquisition or tracking
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

This invention relates to the field of Inertial Navigation Systems (INS) and satellite navigation systems such as the Global Positioning System (GPS) and in particular relates to methods of integrating GPS and INS data in order to provide more accurate navigation solutions. The invention provides a method of improving the accuracy of a tightly coupled integrated INS and satellite radio navigation system in cases where the satellite navigation receiver has adaptive tracking loop bandwidths.

Claims

7-1. A method as claimed in any of claims 1 to 3 wherein the measurement noise covariance, R, is divided by an adaption matrix A as claimed in claim 6.

10. A method of integrating inertial navigation system and satellite navigation system data in a tightly coupled architecture by means of a Kalman filter, the satellite navigation data being received on a receiver having adaptive tracking loop bandwidths, comprising

i) monitoring the tracking loop bandwidths or modelling them as a function of the receiver measured signal to noise density ratio (c/n₀) outputs, and

ii) modelling the correlated measurement noise within the Kalman filter explicitly such that the assumed correlation time is varied in inverse proportion to the tracking loop bandwidths.

11. A method as claimed in claim 10 wherein the correlation time is varied independently for measurements from each tracking loop.

12. A method as claimed in claim 10 wherein the tracking loop bandwidths are calculated from the received signal to noise density ratio.

13. A method as claimed in claims 10 to 12 wherein the correlated measurement noise is estimated as Kalman filter states.

14. A method as claimed in claims 10 to 12 wherein the correlated measurement noise is modelled using a Schmidt-Kalman filter algorithm.