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

• A simplified form of existing approximated likelihood function is given. • A novel explicit expression of logarithm of likelihood function is proposed. • It deals directly with the distribution of the scaling factor. • A feasible weighting scheme is designed. 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. [ABSTRACT FROM AUTHOR]