Abstract
A new robust cubature Kalman filter is proposed using adaptive generalized maximum correntropy (AGMC) criterion rather than the conventional MMSE criterion in this paper. In the proposed method, the adaptive generalized maximum correntropy (AGMC) criterion is firstly constructed from an adaptive forgetting correntropy based cost function, which is rather robust with respect to the process uncertainty and non-Gaussian noise. On this basis, a new robust cubature Kalman filter is further derived, where the predicted state vector and received measurements are processed simultaneously based on the regression form derived via the statistical linearization approach. An adaptive forgetting scheme is then proposed in combination with the AGMC-CKF to update the parameters of the AGMC adaptively in real time. Taking advantage of the AGMC, the unknown noise statistics caused by the process uncertainty and non-Gaussian noise can be effectively suppressed. Simulations and car-mounted experiments demonstrate that the proposed filter is superior in terms of estimation accuracy and robustness as compared with the related state-of-art methods.
Highlights
Because of the complementary properties of the strap-down inertial navigation system (SINS) and global navigation satellite system (GNSS), the integration of SINS and GNSS has become one of the most popular approaches to the position and attitude determination of a moving vehicle [1]–[3]
The cubature Kalman filter (CKF) is developed based on the minimum mean square error (MMSE) criterion and only suitable for the Gaussian system with exact prior knowledge of process noise and measurement noise [6]
Motivated by the maximum correntropy and weighted least square method, we proposed in this paper a new criterion termed adaptive generalized maximum correntropy criterion (AGMC) as follows: JAGMC =
Summary
Because of the complementary properties of the strap-down inertial navigation system (SINS) and global navigation satellite system (GNSS), the integration of SINS and GNSS has become one of the most popular approaches to the position and attitude determination of a moving vehicle [1]–[3]. The high-dimensional nonlinear SINS/GNSS integrated navigation system widely applies the cubature Kalman filter (CKF) featuring satisfactory performance and ease of implementation [4], [5]. Abnormal measurement of GNSS, the process noise of the SINS/GNSS integrated navigation system is hard to obtain and the measurement noise may not follow the Gaussian distribution, causing a negative impact on the system performance [7]–[9]. This work is aimed to develop an effective CKF to increase the estimation accuracy and robustness against both of the uncertain process noise and non-Gaussian measurement noise. The multiple model AKF (MMAKF), a multi-model adaptive estimation method, captures the uncertain process noise by running a bank of Kalman filters with different stochastic models parallelly [10]
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