Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

• Published in: IEEE systems journal Vol. 16; no. 4; pp. 6669 - 6680
• Main Authors: Shi, Chenguang, Dai, Xiangrong, Wang, Yijie, Zhou, Jianjiang, Salous, Sana
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airborne radar; Airborne radar network; Algorithms; Atmospheric modeling; Bayesian Cramér–Rao lower bound (BCRLB); Cramer-Rao bounds; Design optimization; Dwell time; joint route optimization and multidimensional resource management (JRO-MDRM); Kinematics; low probability of intercept (LPI); Lower bounds; Optimization; Optimization techniques; Particle swarm optimization; particle swarm optimization (PSO); Radar; Radar networks; Radar tracking; Resource management; Route optimization; Target tracking; Waveforms
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2021.3134657

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér-Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks.