一种多机动目标协同跟踪的博弈论算法

doi:10.3969/j.issn.1001-2400.2012.06.008

Abstract

Abstract:

For the dynamic tracking problem in sensor networks, a new collaborative tracking algorithm for multiple maneuvering targets based on game theory is proposed. First, the interacting multiple model extended particle filtering algorithm is applied to obtain the estimated state by each sensor in the networks. Then, the target information gain is utilized as a basis for sensor negotiation. When the desired covariance level is updated during target tracking, a new negotiation will start to reallocate resources. The negotiation will allocate more sensors to the negotiation caller for its tracking under the condition of protecting the interests of both sides. Simulation results show that the proposed method can load to the desired tracking accuracy compared with traditional methods in a nonlinear non-Gaussian system.

Key words: game theory, extended Kalman particle filtering, interacting multiple model, collaborative tracking, sensor

CLC Number:

TP391

LIU Qin;LIU Zheng;LIU Jun. Collaborative tracking algorithm for multiple maneuvering targets based on the game theory[J].J4, 2012, 39(6): 49-54+135.

References

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Collaborative tracking algorithm for multiple maneuvering targets based on the game theory

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