在感知不全的条件下，用博弈论方法实现搜索与逃逸的高效对抗。
Mobile Target Search with Imperfect Perception: A Partially Observable Stochastic Game Theoretical Approach
- 采用部分可观测随机博弈模型，融合目标智能与感知不确定性。
- 提出可检测性准则，确保策略最终能发现目标，避免漏检。
- 设计分布式算法，适合多搜索者协同，适用于无人机或机器人场景。
本文研究在传感器限制、恶意干扰或通信噪声导致感知不全情况下的移动目标搜索问题。搜索者与目标在有限移动范围的网格区域内动态博弈，形成搜索与逃逸的对抗。为建模这种在感知不全下的对抗关系，本文采用部分可观测随机博弈（POSG）框架，该框架通过引入目标智能扩展了部分可观测马尔可夫决策过程（POMDP）。针对误报和漏报问题，提出新的可检测性概念，并基于随机遍历分析给出充分可检测性条件。进一步设计一种服务器辅助的分布式算法，利用搜索者的聚合势博弈结构，以及基于KL散度的目标预测简化方法。数值仿真验证了算法有效性，并支持可检测性分析。
原文摘要 · Abstract (English)
This paper investigates mobile target search under imperfect perceptions caused by sensor limitations, malicious jamming, or communication noise. Searchers and targets operate in a grid-shaped area with bounded mobility, leading to a dynamic interplay between search and evasion. To capture this adversarial interaction under imperfect perceptions, we adopt the partially observable stochastic game (POSG) approach, which generalizes partially observable Markov decision processes (POMDPs) by incorporating target intelligence. To handle false alarms and missed detections caused by perceptual uncertainties, we propose a novel detectability concept to determine whether a search strategy guarantees eventual detection, and provide sufficient detectability criteria based on stochastic recurrence analysis. We further develop a server-assisted distributed algorithm that utilizes the aggregative potential game structure for searchers and a KL-divergence-based reduction for target prediction. Numerical simulations validate the effectiveness of the proposed algorithm and support the detectability analysis.
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