title: Extended emitter target tracking using GM-PHD filter.
abstract: If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis density filter for extended target tracking is computationally intractable. To reduce the computational burden, a fast partitioning algorithm based on hierarchy clustering is proposed in this paper. It combines the two most similar cells to obtain new partitions step by step. The pseudo-likelihoods in the Gaussian-mixture probability hypothesis density filter can then be computed iteratively. Furthermore, considering the additional measurement information from the emitter target, the signal feature is also used in partitioning the measurement set to improve the tracking performance. The simulation results show that the proposed method can perform better with lower computational complexity in scenarios with different clutter densities.
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Youqing
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Shilin
authors.initials: S
authors.lastname: Zhou
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Gui
authors.initials: G
authors.lastname: Gao
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Huanxin
authors.initials: H
authors.lastname: Zou
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Lei
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016011:Normal Distribution; D011336:Probability; D011825:Radar; D012815:Signal Processing, Computer-Assisted