A novel particle filter for extended target tracking with random hypersurface model

In the random hypersurface model for extended target tracking problem, the scaling factor in the measurement equation brings difficulty for existing particle filter to calculate the likelihood in the weighting update stage. In this paper, we firstly simplify the existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one. Then, by directly dealing with the distribution of the scaling factor whose square has uniform distribution, we propose a novel explicit formula of the logarithm of likelihood. Based on this formula, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed. Simulation shows that NPFA improves estimation accuracy compared with the existing unscented Kalman filter and particle filter for the tracking problem under discussion.(c) 2022 Published by Elsevier Inc.