Multi-sensor management for data fusion in target tracking

Abstract

Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor management (DSM) scheme is increasingly important in modern multi-sensor systems. DSM is afforded in modern systems through increased bandwidth, wireless communication, and enhanced power. However, protocols for system control are needed to management device access. As game theory offers learning models for distributed allocations of surveillance resources and provides mechanisms to handle the uncertainty of surveillance area, we propose an agent-based negotiable game theoretic approach for decentralized sensor management (ANGADS). With the decentralized sensor management scheme, sensor assignment occurs locally, and there is no central node and thus reduces the risk of whole-system failure. Simulation results for a multi-sensor target-tracking scenario demonstrate the applicability of the proposed approach.