Joint Node Selection and Power Allocation Strategy for Multitarget Tracking in Decentralized Radar Networks

Networked radar systems have been demonstrated to offer enhanced target tracking capabilities. An effective radar resource allocation strategy can efficiently optimize system parameters, leading to performance enhancements. In this paper, two critical but limited system resources are considered for optimization: the number of radar nodes and the transmitted power. In this scenario, a joint node selection and power allocation (JSPA) strategy is developed with the objective of tracking multiple targets. The proposed mechanism implements the optimal resource allocation based on the feedback information in the tracking recursion cycle in order to improve the worst-case tracking accuracy with multiple targets. The network architecture considered in this paper is decentralized so that communication requirements may be reduced while maintaining system robustness. Since the predicted conditional Cramér–Rao lower bound (PC-CRLB) provides a lower bound on the accuracy of the target state estimates conditional on the actual measurement realizations, it is more accurate than the standard posterior CRLB and is thus derived and used as an optimization criterion for the JSPA strategy. It is shown that the optimal JSPA is a two-variable nonconvex optimization problem. We propose an efficient two-step semidefinite programming based solution to solve this problem. Numerical results demonstrate the superior performance of the proposed strategy and the effectiveness of the proposed solution.