Control and Optimization in Applied Mathematics

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

Articles in Press

Document Type : Research Article

Authors

1 Department of Electrical and Computer Engineering‎, ‎University of Science and Technology of Mazandaran‎, ‎Behshahr‎, ‎Iran

2 Department of Applied Mathematics‎, ‎University of Science and Technology of Mazandaran‎, ‎Behshahr‎, ‎Iran‎..

10.30473/coam.2025.72829.1272

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 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.

Highlights

  • Introduced a novel algorithm that optimally sets the signal duration, transmission power, and radar-target assignments to enhance target detection and velocity estimation accuracy.
  • Formulated the resource allocation problem to minimize target velocity estimation MSE under constraints of detection performance and power budget.
  • Employed a genetic algorithm to derive an approximate optimal solution, effectively addressing the non-convex and NP-hard characteristics of the problem.
  • Simulation results showcase the algorithm's superior detection and tracking accuracy performance compared to existing benchmark methods.

Keywords

Main Subjects

References
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Control and Optimization in Applied Mathematics

Articles in Press, Accepted Manuscript
Available Online from 21 February 2025
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