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Abstract
With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effectively restrain its influence on navigation solution, leading to improved navigation performance for the INS/BDS integration.
Highlights
Nowadays, the inertial navigation system (INS) plays an important role in the vehicle navigation community due to its autonomy and comprehensive navigation information [1, 2]
Is paper presents a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to improve the adaptability of INS/Beidou-3 system (BDS) integration. e proposed method establishes a theory of an estimation model for the measurement noise covariance based on the maximum likelihood principle
An error model with nonlinear characteristics is established based on the additive quaternion, and the differences of velocity and position information between INS and BDS are taken as the measurement of the integrated system. us, the system model for INS/BDS integration is developed and described in the following
Summary
The inertial navigation system (INS) plays an important role in the vehicle navigation community due to its autonomy and comprehensive navigation information [1, 2]. INS/GNSS integration is a promising solution to improve the navigation and positioning accuracy by utilizing the GNSS information to compensate the error of the INS. The most existing maximum likelihood method is only for the Kalman filter in a linear system, there has been very limited research focusing on the use of the maximum likelihood principle for system noise statistic estimation in a nonlinear CKF. Is paper presents a method of CKF with the measurement noise covariance estimation by using the maximum likelihood principle to improve the adaptability of INS/BDS integration. An error model with nonlinear characteristics is established based on the additive quaternion, and the differences of velocity and position information between INS and BDS are taken as the measurement of the integrated system. Ωu (ωbib), Ωd (ωnin) U (Qnb), Y (Qnb) f b, δf b ωnie, ωnen Vn, δωie, δωnen δωbib δωnin
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