Adaptively Robust Unscented Kalman Filter for Tracking a Maneuvering Vehicle

Robust unscented Kalman filter with adaptation of process and measurement noise covariances

“…Since the process noise covariance matrix is usually unknown (e.g., tracking a maneuvering vehicle [40]), the covariance matrix should be adjusted recursively. To derive the estimated covariance Q k−1 , an adaptive factor is used to adjust the covariance as follows [43] Q k−1 = λ k−1Q k−1 (28) where Q k−1 is the pre-designed covariance and λ k is the adaptive factor calculated by…”

“…The application in nanosatellites attitude estimation shows that the robust UKF is fault tolerant against the sensor malfunctions. In [40], the fading factor is adopted to reduce the effect of the dynamics model errors and a robust estimation strategy is introduced to suppress the measurement model errors. Although the effect of the dynamic model error and the measurement error can be reduced simultaneously, the process and the measurement noise should be Gaussian distributions with known covariance matrices [37][38][39][40].…”

“…The main advantage of the UKF is that it does not use any linearization for calculating the state predictions and covariances. Many robust UKF variants have been proposed [30][31][32][33][34][35][36][37][38][39][40]. For instance, the H ∞ performance criterion has been combined with the UKF to improve the robustness against model errors and noise uncertainty in [30].…”

Robust Student’s t-Based Stochastic Cubature Filter for Nonlinear Systems With Heavy-Tailed Process and Measurement Noises

“…To solve the filtering problem of nonlinear state-space model with heavy-tailed non-Gaussian noises, the Huber-based nonlinear Kalman filter (HNKF) has been proposed by minimising a Huber cost function that is a combined l 1 and l 2 norm [20]. A larger number of variants of the HNKF have been derived based on a linearized or statistical linearized method, such as the Huber-based extended Kalman filter [21], the Huber-based divided difference filter [22], the Huber-based unscented Kalman filter [23], the nonlinear regression Huber Kalman filter [24] and the adaptively robust unscented Kalman filter (ARUKF) [25]. However, the influence function of the HNKFs don't redescend, which may deteriorate the estimation performance of the HNKFs [12].…”

Improved adaptive unscented Kalman filter algorithm for target tracking

“…The adaptive filtering algorithm can be divided into three categories: scaling state covariance matrix (P-matrix), multi-model adaptive estimation and adaptive stochastic model modeling. [7] proposed a filtering algorithm of scaling the process noise covariance matrix Q. It is believed that the tuning parameters are less and the robustness is better than the algorithm of scaling the state covariance matrix.…”