Abstract
To estimate the systems with one-step randomly delayed measurement and time-correlated heavy-tailed measurement noises, on the basis of robust Student’s t based unscented filter (RSTUF), an improved Student’s t based unscented filter (ISTUF) is proposed. Referring to the measurement differencing method, a reformed measurement model was built. Then, by augmenting the system state vector, the conditional probability distribution of the measurement noise with respect to delayed measurement was taken into consideration. Based on the reformed measurement model and the augment state vector, a novel estimator was designed to solve the one-step randomly delayed problem. Maneuvering target tracking systems were used for simulation. Compared with unscented Kalman filter (UKF) or RSTUF, ISTUF had higher accuracy.
Highlights
In recent years, as a state estimation tool, a Kalman filter (KF) has been successfully applied in the target tracking system
Maneuvering target tracking systems were used for simulation
From Equation (28), we find that the computation of E e yke yk |Y∗k−1 depends on the computation of E ηk−1 ηTk−1 |Y∗k−1
Summary
As a state estimation tool, a Kalman filter (KF) has been successfully applied in the target tracking system. To estimate systems with non-Gaussian noises, Huber-based Gaussian filters (HGFs) have been proposed. Can deal with non-Gaussian noise and the estimation accuracy is higher than the filters in a GF framework [8,9]. The random time-delayed measurement may affect the accuracy of the shadowing filter. Proposed an unscented filter for two-step randomly delayed measurement. Based on Gaussian mixture approximation, Gu [22] designed a filter to deal with the d-step state-delay problem. By state augmentation and projection theory, an optimal filter was designed for systems with one-step delayed measurement [23]. Summarizing the above discussions, the objective of the work was to estimate the states of the systems with one-step delayed measurement and time-correlated heavy-tailed noises accurately. Based on the measurement differencing approach, the time-correlated part of noises was eliminated.
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