Hierarchical Task and Power Coalition Game of Radars for Maneuvering Target Tracking in Self-Defensive Suppressive Jamming
Kui Xiong, Guolong Cui, Maosen Liao, Shilong Li, Lingjiang Kong
Abstract
Conventional optimization methods have been presented to solve the cooperative target tracking problem of radars without considering the task preference and the resource cost of the individual radar simultaneously, thus leading to robustness deficiency and performance degradation in dynamic jamming environments. To address this problem, the cooperative task assignment and power allocation problem of netted radars for maneuvering target tracking in self-defensive suppressive jamming is characterized as a decision-making and control process of multiple autonomous agents, and the multiagent cooperative tracking and control framework is therefore developed constituting with state prediction, coalition formation, power allocation, and state estimation. To characterize the preferences of radars for tracking decision, the utility function consisting of the reward function under the quality of service framework and the cost function with considering system resources is devised. Then, the hierarchical coalition game is formulated to describe the cooperative task assignment and power allocation process of radars, and the existence of the stable coalition partition of the game is proven. Based on the developed game model, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alternating coalition formation and projection gradient</i> (ACFPG) algorithm is proposed leveraging the interacting multiple model filter for maneuvering target tracking. Numerical simulations are performed to illustrate the superiorities of the proposed strategy against some counterparts in terms of tracking utility and resource reduction.