Abstract
to the vehicle’s severe maneuver and abnormal measurements of GPS in practical applications, the statistic of process noise in SINS/GPS integrated navigation system may be unknown and the measurement noise may not follow the Gaussian distribution, which results in a deteriorated performance for the conventional cubature Kalman filter. To address this issue, we propose in this paper a new robust cubature Kalman filter based on the adaptive information entropy theory. In the proposed filter, the process uncertainty and non-Gaussian measurement noise are simultaneously suppressed based on a new constructed cost function using the maximum correntropy and residual orthogonal principle based weighted least squares technology, which is independent of noise distribution and more insensitive to the non-Gaussian noise. Moreover, a multiple-channel adaptive strategy for the better process uncertainty suppression is given. Furthermore, some improvements are proposed to avoid the numerical problem and implement the proposed robust filter effectively. Extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods.
Highlights
SINS/GPS integration navigation system has achieved considerable penetration in the civilian and military field in recent years due to its widespread application in positioning and attitude determination [1]–[6]
Motivated by the cost function of weighted least square (WLS), we propose to use the residual orthogonal principle-based WLS method to deal with process uncertainty noise and the maximum correntropy criterion to handle the non-Gaussian measurement noise, and construct the new cost function termed adaptive maximum correntropy criterion (AMCC) as follows: JL =
Remark 1: As one can observe that, different from the conventional maximum correntropy criterion (MCC) adopted in [29], [31] which is only suitable for the suppression of non-Gaussian measurement noise under the condition that the process distribution is Gaussian, our proposed AMCC use the advantage of residual orthogonal principle based weighted least square technology to further improve the robustness against both of the process uncertainty and non-Gaussian measurement noise without the consideration of process distribution, which can be appropriately applied to the loosely coupled SINS/GPS integrated navigation system with the time-varing process uncertainty
Summary
SINS/GPS integration navigation system has achieved considerable penetration in the civilian and military field in recent years due to its widespread application in positioning and attitude determination [1]–[6]. Compared with the existing MCC-based robust filtering, in the proposed RCKF, the novel cost function named AMCC using residual orthogonal principle-based weighted least squares technology is first presented based on the improvement of the original cost function [16]; it can simultaneously handle the process uncertainty and non-Gaussian measurement noise in the nonlinear SINS/GPS integrated navigation model. We utilize the maximum correntropy criterion and residual orthogonal principle-based weighted least square technology to derive a new robust cubature Kalman filter (RCKF) in this work This new RCKF may perform better in the presence of both process uncertainty and non-Gaussian measurement noise since the correntropy contains the high order information of error and the residual orthogonal principle can extract the useful information in the residual error completely
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