Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

被引:8
|
作者
Dai, Jinhui [1 ]
Pu, Wenqiang [2 ]
Yan, Junkun [1 ]
Shi, Qingjiang [2 ,3 ]
Liu, Hongwei [1 ]
机构
[1] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China
[2] Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
[3] Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China
基金
中国国家自然科学基金;
关键词
Asynchronous target tracking; passive sensor; resource allocation; trajectory optimization (TO); unmanned aerial vehicle (UAV); TARGET TRACKING; PATH DESIGN; LOCALIZATION;
D O I
10.1109/TGRS.2023.3239952
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramer-Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.
引用
收藏
页数:16
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