Resource management strategy for maneuvering multi-target tracking in cognitive radar network under non-ideal communication

A joint node selection and power allocation (JNSPA) resource management strategy is proposed in this manuscript to minimize the total system power consumption in cognitive radar network (CRN) under non-ideal communication, while maintaining desired tracking accuracy. Firstly, the predicted conditional Cramér-Rao Lower Bound (PC-CRLB) under non-ideal communication is derived for evaluating tracking performance, where the impact of data loss between local radars (LRs) and fusion center (FC) is comprehensively considered. In the derivation of the PC-CRLB, the data information from position measurement and pseudo-measurement are taken into consideration sequentially to match the following adaptive linear sequential filtering algorithm. Furthermore, an optimization model is formulated to minimize the total system power consumption, subject to system constraints. Due to the coupling of optimization variables and the inclusion of binary variables, the problem is a mixed-integer nonlinear optimization problem with NP-hard characteristics. To solve this, an alternating optimization method based on task priority and node contribution (TPNC) algorithm is proposed for efficient solution. Finally, simulation results demonstrate that the resource management strategy generated by FC can effectively guide the LRs, validating the effectiveness, superiority, and real-time applicability of proposed JNSPA strategy.