Time-matching extended target probability hypothesis density filter for multi-target tracking of high resolution radar

Abstract

Abstract Extended target probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-target tracking. However, in the classic ET-PHD filters, the measurements of different targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance.