Linear multitarget integrated probabilistic data association for multiple detection target tracking

Abstract

The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multiple detection problem. The typical algorithms with the point target assumption are not eligible for multiple detection tracking environments. The multiple detection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multiple detection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multiple detection multitarget tracking scenarios.