The LFT based PHD filter for nonlinear jump Markov models in multi-target tracking
Syed Ahmed Pasha, Hoang Duong Tuan, Pierre Apkarian · 2009
The probability hypothesis density (PHD) filter is a computationally viable solution for tracking an unknown, and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and miss-detection. This paper presents a PHD filter for a broad class of problems by accommodating targets that follow nonlinear jump Markov system (JMS) models. Our approach is based on the framework of the virtual linear fractional transformation (LFT) model which has shown great potential in single target filtering applications. Simulation results demonstrate that the proposed PHD filtering algorithm is robust for tracking multiple maneuvering targets.