Control and Optimization
Maryam Najimi; Akbar Hashemi Borzabadi
Abstract
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 ...
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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.
