Radar Selection Based on the Measurement Information and the Measurement Compensation for Target Tracking in Radar Network
Xueting Li, Tianxian Zhang, Wei Yi, Lingjiang Kong, Xiaobo Yang · IEEE Sensors Journal · 2019
Radar network has been a hot topic in recent years for the plenty of resources it can use, especially in tracking applications. A popular problem arises, that is how to improve the tracking performance with less system resources. In this paper, we study the problem of radar selection in a radar network to decrease the resource consumption and improve the tracking performance. First, problem formulation is given to describe the system model of the radar network. Second, based on the characteristics of radar system, a radar selection method considering both the measurement information and the measurement compensation is proposed. Third, as the proposed radar selection problem is a NP-hard problem, by deploying the semidefinite relaxation (SDR) method, the optimal radar selection problem can be relaxed from a NP-hard problem to a semidefinite programming (SDP) problem, resulting in a more efficient computation, especially in the case of large scale problem. Finally, the numerical simulations for target tracking based on the proposed radar selection method are provided to verify the validity and effectiveness of the proposed method.