Mobile Target Search with Imperfect Perception: A Partially Observable Stochastic Game Theoretical Approach
For multi-agent search and evasion scenarios with sensor limitations, this work provides theoretical detectability guarantees and a practical algorithm, though it is an incremental extension of existing game-theoretic and POMDP methods.
This paper addresses mobile target search under imperfect perception (false alarms, missed detections) using a partially observable stochastic game framework. It proposes a detectability concept with sufficient criteria and a server-assisted distributed algorithm, validated by simulations.
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.