Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System
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...
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| Published in | IEEE transactions on intelligent transportation systems Vol. 25; no. 9; pp. 11422 - 11438 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1524-9050 1558-0016 |
| DOI | 10.1109/TITS.2024.3367892 |
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| 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. |
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| AbstractList | 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. |
| Author | Chang, Zheng Hu, Fengye Wang, Bo Feng, Zhiyuan Yang, Jiahui Zhao, Yanping |
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| Snippet | Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as... |
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| SubjectTerms | alternating optimization Autonomous aerial vehicles joint trajectory planning and transmit resource optimization (JTPTRO) MIMO radar multi-target tracking (MTT) Multi-UAV-enabled MIMO radar system Optimization posterior Cramér-Rao lower bound (PCRLB) Power demand Radar Radar tracking Target tracking |
| Title | Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System |
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