Resource Allocation Optimization for Multi-Target Detection and Tracking in Cognitive Radar Networks

This paper addresses the challenges of power control‎, ‎radar assignment, and signal timing to improve the detection and‎ ‎tracking of multiple targets within a mono-static cognitive‎ ‎radar network‎. ‎A fusion center is utilized to integrate target‎ ‎velocity data gathered by radars‎. ‎The primary objective is to‎ ‎minimize the mean square error in target velocity estimation while‎  ‎adhering to constraints related to global detection probability and‎ ‎total radar power consumption for effective target detection and‎ ‎tracking‎. ‎The optimization problem is formulated and a low-complexity method is proposed using the genetic algorithm (GA)‎. ‎In‎ ‎this approach‎, ‎the radars and their transmission powers are‎ ‎represented as chromosomes and the network's quality of service‎ ‎(QoS) requirements serve as inputs to the GA‎. ‎The output of the GA‎ ‎is the mean error square of the target velocity estimation‎. ‎Once the‎ ‎problem is resolved‎, ‎the power allocation for each radar assigned to‎ ‎a specific target is determined‎. ‎Simulation results demonstrate the‎ ‎effectiveness of the proposed algorithm in enhancing detection‎ ‎performance and improving tracking accuracy when compared to other‎ ‎benchmark algorithms‎.