Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

• Published in: IEEE transactions on aerospace and electronic systems Vol. 58; no. 4; pp. 2762 - 2778
• Main Authors: Shi, Chenguang, Wang, Yijie, Salous, Sana, Zhou, Jianjiang, Yan, Junkun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayesian Cramér–Rao lower bound (BCRLB); Cramer-Rao bounds; distributed phased array radar network; Doppler effect; Dwell time; joint transmit resource management and waveform selection (JTRMWS); low probability of intercept (LPI); Lower bounds; Luminance distribution; Optimization; Optimization models; Optimization techniques; Particle swarm optimization; Phased arrays; Radar; Radar arrays; Radar cross-sections; Radar networks; Radar tracking; Resource management; Storms; Target tracking; Tornadoes; Tracking; Waveforms
• DOI: 10.1109/TAES.2021.3138869
• ISSN: 0018-9251

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks.