Multi-target identity management with decentralized optimal sensor scheduling

Abstract

This paper proposes a multi-target identity management algorithm with two types of sensors: a primary sensor which has a large detection range to provide the targets’ state estimates, and multiple secondary sensors which are capable of recognizing the targets’ identities. Each of the secondary sensors is assigned to a sector of the operation area. For the secondary sensors, we develop a sensor scheduling strategy composed of two parts: deciding which target to be identified and controlling the secondary sensor to identify the selected target, by formulating an optimization problem to minimize the uncertainty of the targets’ identities subject to the sensor dynamic constraints. In addition, a feedback term is included in the secondary sensor control to compensate for the modeling/measurement error of the targets’ states. The proposed algorithm is decentralized in that the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the above with the existing multi-target tracking algorithms, we develop a robust closed-loop multi-target identity management algorithm with guaranteed performance. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.