Resource Optimization Strategy in Phased Array Radar Network for Multiple Target Tracking When Against Active Oppressive Interference

Abstract

The phased array radar network has the ability to track multiple targets simultaneously through the cooperative work of several active nodes, which brings significant advantages in tracking target under oppressive interference. In this article, an efficient resource optimization strategy (ROS) is developed in this scenario. The optimization model is formulated with the aim of countering active oppressive interference by dynamically assigning tracking tasks and allocating transmit power to each node. The architecture of information fusion considered in this article is centralized so that the detection performance and the parameter estimation accuracy can be improved. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) can provide an accurate performance lower bound for target tracking accuracy, the objective function is designed to improve the sum of weighted PC-CRLBs of multiple targets. By introducing the relaxation technique and the sorting algorithm, this mixed integer nonlinear programming problem is converted into a two-stage nonconvex problem, and the efficient solutions are obtained through the modified Zoutendijk method of feasible directions algorithm. Numerical simulations demonstrate the effectiveness of the proposed ROS.