Multitarget Tracking by Improved Particle Filter Based on Unscented Transform

Abstract

This paper considers the problem of multitarget tracking in cluttered environment. To reduce the dependency on the noise priori knowledge, an improved particle filtering (PF) data association approach is presented based on the ∞ filter (HF). This approach can achieve higher robustness in the condition that the measurement noise prior is unknown. Because of the limitations of the HF in nonlinear tracking, we first present the ∞ unscented filter (HUF) by embedding the unscented transform (UT) into the ∞ extended filter (HEF) structure. Then the HUF is incorporated into the Rao-Blackwellized particle filter (RBPF) framework to update the particles. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms in linear and nonlinear multitarget tracking.

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.…”