4.6 Article

Quality of Service Constrained-Resource Allocation Scheme for Multiple Target Tracking in Radar Sensor Network

期刊

IEEE SYSTEMS JOURNAL
卷 15, 期 1, 页码 771-779

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2020.2990409

关键词

Quality of service; Radar tracking; Target tracking; Optimization; Resource management; Convex functions; Multiple target tracking (MTT); quality of service (QoS); radar sensor network; resource allocation (RA)

资金

  1. National Natural Science Foundation of China [61701379]
  2. National Science Fund for Distinguished Yong Scholars [61525105]
  3. Fund for Foreign Scholars in University Research and Teaching Programs 111 project [B18039]
  4. Natural Science Foundation of Shaanxi Province [2020JQ-297, 2017CGZH-RGXQ-02, 2017CGZH-RGGJ-02]

向作者/读者索取更多资源

In this article, a quality of service constrained-resource allocation (QoSC-RA) scheme is proposed for multiple target tracking in radar sensor network. The scheme divides radar sensors into groups and optimizes transmit resources to minimize total resource consumption while achieving desired MTT accuracy. Simulation results show that the QoSC-RA process can achieve predetermined MTT performance with smaller resource consumption compared to the uniform allocation scheme.
In this article, a quality of service constrained-resource allocation (QoSC-RA) scheme is built for multiple target tracking (MTT) in radar sensor network. This QoSC-RA scheme can not only divide radar sensors into several groups to illuminate multiple targets separately but also optimize the transmit resource of the radar sensors in different groups. In such a case, we can minimize the total resource consumption of a radar sensor network while achieving the desired MTT accuracy. The QoSC-RA scheme is formulated as a mathematical optimization problem and is shown to possess two sets of conflict constraints (the QoS constraints and the resource constraints). When the problem is feasible, we analyze its optimality based on the Karush-Kuhn-Tucker conditions and subsequently design appropriate solution methods to solve it efficiently. When the problem is infeasible, we relax it as a feasible one by enlarging its feasible domain and use a gradient projection method to achieve a compromise solution. Simulation results demonstrate that the QoSC-RA process can achieve predetermined MTT performance with smaller resource consumption than the uniform allocation scheme.

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