Abstract
The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.
Highlights
GPS/INS integration navigation systems have drawn great attention due to their widespread applications in the areas of dynamic navigation and positioning [1,2,3]
The conventional strong tracking filter may suffer from the following problems: (1) The suboptimal fading factor is incorporated in the whole filtering process, which may result in the loss of precision when there is no process uncertainty existed; (2) The symmetry of the prediction covariance matrix cannot be guaranteed when the suboptimal fading factor with different diagonal elements is carried out on the error covariance matrix, which may degrade the filtering performance and even lead to the divergence
In order to compare the overall performance of the four methods and intuitively illustrate the effectiveness of the proposed filter for low-cost GPS/INS integration, the root mean square error (RMSE) of the velocity and attitude that averaged across all time instances is used as a comparison metric, which are defined as follows: s
Summary
GPS/INS integration navigation systems have drawn great attention due to their widespread applications in the areas of dynamic navigation and positioning [1,2,3]. The influence function of the HKF doesn’t redescend, which results in limited estimation accuracy [20] Another way of solving the problems of process uncertainty is to use adaptive filters where the online estimates of process noise statistics are obtained together with the dynamic state. The conventional strong tracking filter may suffer from the following problems: (1) The suboptimal fading factor is incorporated in the whole filtering process, which may result in the loss of precision when there is no process uncertainty existed; (2) The symmetry of the prediction covariance matrix cannot be guaranteed when the suboptimal fading factor with different diagonal elements is carried out on the error covariance matrix, which may degrade the filtering performance and even lead to the divergence. The experiment results indicate that the proposed IST-7thSSRCKF method has better robustness for the suppression of process uncertainties as compared with CKF and STCKF for the GPS/INS integrated navigation problem.
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