A1 Journal article (refereed)
Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System (2024)

Yang, J., Wang, B., Chang, Z., Zhao, Y., Feng, Z., & Hu, F. (2024). Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System. IEEE Transactions on Intelligent Transportation Systems, Early Access. https://doi.org/10.1109/TITS.2024.3367892

JYU authors or editors

Publication details

All authors or editors: Yang, Jiahui; Wang, Bo; Chang, Zheng; Zhao, Yanping; Feng, Zhiyuan; Hu, Fengye

Journal or series: IEEE Transactions on Intelligent Transportation Systems

ISSN: 1524-9050

eISSN: 1558-0016

Publication year: 2024

Publication date: 12/03/2024

Volume: Early Access

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TITS.2024.3367892

Publication open access: Not open

Publication channel open access: 

Abstract

Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as self-driving, surveillance, and navigation. To enhance the MTT performance, the unmanned aerial vehicles (UAVs) have emerged as effective assistants to MIMO radar system, due to their advantages of high flexibility, controllable deployment and cost-effectiveness. Towards this end, this work investigates a multi-UAV-enabled MIMO radar system, in which each UAV is equipped with a MIMO radar unit and dispatched to track multiple targets simultaneously. We are interested in the joint trajectory planning and transmit resource optimization (i.e. radar waveform optimization and transmit power allocation) to minimize the system power consumption, subject to constraints related to UAVs motion, system resources, and tracking accuracy. Specifically, the posterior Cramér-Rao Lower Bound (PCRLB) is derived and employed as a guideline for the joint optimization. Given the non-convex and inter-variable coupling nature of the formulated problem, we decompose it into three sub-problems and design an alternating optimization method. Firstly, for the UAVs trajectory planning, we obtain sub-optimal results leveraging the successive convex approximation (SCA)-based algorithm. Next, we present a feasible solution set for radar waveform optimization. For transmit power allocation, we perform a convex transformation and find the numerical solution. In addition, through introducing the Lagrange dual method, we further obtain the optimal analytical solution. Finally, simulation results demonstrate the effectiveness and advantages of the developed strategy.

Keywords: autonomous cars; traffic control; routing; remote monitoring; optimisation; radars; unmanned aerial vehicles

Free keywords: Multi-UAV-enabled MIMO radar system; joint trajectory planning and transmit resource optimization (JTPTRO); multi-target tracking (MTT); posterior Cramér-Rao lower bound (PCRLB); alternating optimization

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2024

Preliminary JUFO rating: 2