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

被引:81
|
作者
Shi, Chenguang [1 ]
Wang, Yijie [1 ]
Salous, Sana [2 ]
Zhou, Jianjiang [1 ]
Yan, Junkun [3 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Minist Educ, Key Lab Radar Imaging & Microwave Photon, Nanjing 210016, Peoples R China
[2] Univ Durham, Sch Engn & Comp Sci, Durham DH1 3LE, England
[3] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Target tracking; Radar tracking; Radar; Resource management; Optimization; Phased arrays; Radar cross-sections; Bayesian Cramer-Rao lower bound (BCRLB); distributed phased array radar network; joint transmit resource management and waveform selection (JTRMWS); low probability of intercept (LPI); target tracking; MULTITARGET TRACKING; POWER ALLOCATION; LOW PROBABILITY; MIMO RADAR; BANDWIDTH ALLOCATION; OPTIMIZATION; FRAMEWORK;
D O I
10.1109/TAES.2021.3138869
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
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 Cramer-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.
引用
收藏
页码:2762 / 2778
页数:17
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