ISAR Imaging Task Allocation for Multi-Target in Radar Network Based on Potential Game

• Published in: IEEE sensors journal Vol. 19; no. 23; pp. 11192 - 11204
• Main Authors: Liu, Xiao-Wen, Zhang, Qun, Luo, Ying, Chen, Yi-Jun, Lu, Xiaofei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Distribution channels; Game theory; Games; Image resolution; Imaging; Inverse synthetic aperture radar; multi-target imaging; Optimization; Radar beams; Radar imaging; Radar network; Radar networks; Resource management; Strategy; Target recognition; task allocation optimization; Task analysis; Vertical integration
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2936423

In this paper, a task allocation strategy for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network is proposed by using the potential game theory. The multi-target ISAR imaging task allocation problem in this paper refers to that the targets are in different radar beams and the purpose is to accomplish all the imaging tasks with high resolution within the limited time. First of all, the multi-target imaging task allocation problem is formulated. According to the imaging task allocation problem, a resolution optimization game is proposed to search for a satisfied strategy profile for all the targets, and the proposed game is proven to be an exact potential game which has at least one Nash equilibrium (NE). The optimal and suboptimal solution of the imaging task allocation problem can converge to a pure-strategy NE. Then, a best response-based imaging task allocation algorithm is proposed to solve the proposed game model. The simulation experiments demonstrate that the proposed game approach which can achieve a satisfied strategy profile for the targets with a lower computation complexity is an effective method for the multi-target imaging task allocation problem.