Multi-sensor cooperative search decision method and device based on bayesian stackelberg game
By using a Bayesian Stackelberg game model, the multi-sensor system is treated as the leader and the target object as the follower. Their mutual influence in actual search is simulated, and the sensor heading combination is optimized. This solves the problem of decision accuracy caused by the target object's active avoidance behavior, and achieves higher collaborative search accuracy and robustness.
Patent Information
- Application Number
- CN202610340384.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-19
- Publication Date
- 2026-07-03
AI Technical Summary
In existing technologies, multi-sensor collaborative search strategies fail to effectively consider the active avoidance behavior of target objects, resulting in a large discrepancy between mathematical models and actual conditions, which affects the accuracy of decision-making.
A Bayesian Stackelberg game-based approach is adopted, with the multi-sensor system as the leader and the target object as the follower. A game model is established, and the mutual influence between the two parties during the actual search process is simulated by adjusting the leader and follower strategies. A cooperative detection probability model and payoff function are constructed, and the game equilibrium is solved to optimize the sensor heading combination.
It improves the accuracy of multi-sensor collaborative search decision-making, enhances the accuracy of leader hybrid strategies by simulating the active avoidance behavior of target objects, overcomes the problems of initial belief bias and local optima, and improves the robustness and accuracy of decision-making.
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