Local feedback compensation method for INS/GPS/OD land navigation system

Accuracy and reliability are important performance indexes for integrated land navigation system. No-reset federal Kalman filter (FKF) developed by Carlson has optimal fault-tolerance performance, but error divergences as long time navigation, which is not suitable for practical land navigation system with inertial navigation system, GPS, and odometer (INS/GPS/OD). In this paper, a new local feedback FKF algorithm is propose to improve the accuracy of land system. There are two local filters in the schematic of local feedback FKF, one is INS/GPS, and the other is INS/OD. Each local filter employs its own inertial navigation update (INU), but shares the same Inertial Measurement Unit (IMU) output information. The output of local filters are no longer the linear, local-optimal estimations of common state vectors as in no-reset FKF, but the linear, local-optimal estimations of common navigation parameters. The master filter takes advantages of the parameter estimations and their error covariance to implement global optimal fusion after local feedback compensation. The feedback compensation can depress the nonlinear error accumulation of local filters, furthermore, the independence of local filters guarantees optimal reliability of the land system. Ground based navigation tests were carried out, the results of which verify the correctness and effectiveness of the improvement technique.