Improved Student’s t-Based Unscented Filter and its Application to Trajectory Estimation for Maneuvering Target

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.

Application of Improved Simplex Quadrature Cubature Kalman Filter in Nonlinear Dynamic System

“…Nonlinear filtering and estimation based on Bayes framework have been widely applied in many fields, such as target tracking [1][2][3][4][5], navigation [5,6] and positioning [7,8], power system [9][10][11], and pattern recognition [12]. Recent years have seen a surge of interest in solving the nonlinear estimation problems.…”