A standard PHD filter for joint tracking and classification of maneuvering extended targets using random matrix

Abstract

This paper presents a novel filter for jointly tracking and classification (JTC) of maneuvering extended targets using the standard probability hypothesis density (PHD) framework. For an extended target, the extended state that describes the target size, shape and orientation is also estimated, in addition to the kinematic state. Assuming that the target size information is known in advance, the presented filter can classify the extended target based on different sizes, instead of based on different kinematic motion modes in point target tracking. By utilizing the known target size information, the presented filter can contribute to a better extended state estimation while classifying, and how a good classification result can improve the estimation is mathematically analyzed. Simulation results show that the presented filter simultaneously provides a superior tracking performance and the correct classification of multiple extended targets, compared to the gamma Gaussian inverse Wishart PHD (GGIW-PHD) filter.