Cognitive FDA-MIMO Radar Network for Target Discrimination and Tracking With Main-Lobe Deceptive Trajectory Interference
Biao Yang, Shengqi Zhu, Xiongpeng He, Lan Lan, Ximin Li
Abstract
This article proposes a cognitive frequency diversity array (FDA) multiple-input multiple-output (MIMO) (FDA-MIMO) radar network (CFDA-MIMON) target discrimination and tracking algorithm under main-lobe deceptive trajectory interference. This algorithm can be divided into two stages: search-discrimination-initialization and cognitive target tracking. The first stage mainly uses the distribution characteristics of target and main-lobe deceptive trajectory interference on different radars to discriminate the target and interference in the radar network. A target state initialization strategy driven by G-pair measurements with large time interval is proposed for target tracking. In the second stage, a cognitive target tracking algorithm of single FDA-MIMO radar system is designed based on the criteria of maximum Capon power spectrum. In addition, in order to avoid the divergence problem of the extended Kalman filter, a target state correction method based on auxiliary particles is proposed. Finally, the fusion strategy of multiple radar trajectories is used to alleviate the estimation error of the single radar. Numerical experiments verify that the proposed CFDA-MIMON target discrimination and tracking algorithm can distinguish the target from main-lobe deceptive trajectory interference without prior information of the target, and output the target's trajectory.