Decentralized Multi-sensor Scheduling for Multi-target Tracking and Identity Management

This paper proposes a multi-target tracking and identity management method with multiple sensors: a primary sensor with a large detection range to provide the targets' state estimates, and multiple secondary sensors capable of recognizing the targets' identities. Each of the secondary sensors is assigned to a sector of the operation area; a secondary sensor decides which target in its assigned sector to be identified and controls itself to identify the target. We formulate the decision-making process as an optimization problem to minimize the uncertainty of the targets' identities subject to the sensor dynamic constraints. The proposed algorithm is decentralized since the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the proposed algorithm with the existing multi-target tracking algorithms, we develop a closed-loop multi-target tracking and identity management algorithm. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.