Gaussian‐like measurement likelihood based particle filter for extended target tracking.

Extended target tracking based on the star‐convex model is a multi‐dimensional nonlinear estimation problem. To approximate the shape of an extended target, the star‐convex model requires a higher‐order Fourier series expansion, which will increase the dimensionality of the extended state, so that the nonlinear filters under Gaussian assumptions cannot converge to the optimal state estimation. In this paper, a novel particle filter algorithm based on Gaussian‐like measurement likelihood (GL) is proposed. As latent variables with high uncertainty in the measurement model, the scattering centres are modelled as a Gaussian‐like probability distribution characterising the target's shape. Then the measurement noise is integrated into the Gaussian‐like probability distribution via Gaussian quadrature to obtain the accurate measurement likelihood. Moreover, particle swarm optimization is employed to reduce computational complexity. It enables particles to move into the vicinity of measurements, thereby increasing the diversity of the particles belonging to the target's shape. The simulation results show that the GL can improve the accuracy of shape estimation under high measurement noise and low measurement rates. Furthermore, the proposed particle filter can track the extended target effectively with an aeroplane‐like shape. [ABSTRACT FROM AUTHOR]