Joint tracking and classification of extended object based on support functions

This paper is devoted to joint tracking and classification of an extended object using measurements of down-range and cross-range extent. Most existing approaches only focus on tracking, which provides estimation of both the centroid state and object extension. However, target classification is also a critical problem in practice. Especially for extended objects, tracking and classification should be handled jointly instead of separately because they affect each other in many practical applications. This paper attempts to solve the problem of joint tracking and classification of extended objects by integrating prior size and extension information into support-function-based object models. The support function fits well with our problem because not only can it describe object shape, but it also has a close connection with the target range extent measurements. An algorithm for joint tracking and classification of extended objects based on support functions is derived to obtain jointly the estimation of kinematic state and object extension in a class and the probability of the object class. Furthermore, we also propose a method for fusing object extension. The effectiveness of the proposed approach is illustrated by simulation results.