Joint threshold optimization and power allocation of cognitive radar network for target tracking in clutter

Abstract Networked radar systems have the ability to enhance the tracking performance by working in a cognitive manner. In the cognitive radar network, resource allocation technique can be employed to improve the system performance. In this paper, a joint threshold optimization and power allocation (JTOPA) strategy for target tracking by radar network in clutter is proposed containing two main stages. At the detection stage, we introduce the Bayesian tracker-aware detector to adaptively adjust the threshold based on the predicted target state information. At the transmitting stage, the limited power resource is optimally assigned to each radar node. By incorporating the improved information reduction factor (IIRF), the Bayesian Cramer-Rao lower bound (BCRLB) is utilized as the optimization criterion for the JTOPA strategy. The JTOPA is a two-variable nonconvex optimization problem. We develop a spectral projected gradient based method to solve the problem. Simulations show that the proposed JTOPA strategy effectively improves the tracking performance compared with the uniform threshold setting and power allocation in the cognitive radar network.