Efficient Control of Information Flow for Distributed Multisensor Fusion Using Markov Decision Processes

Network-centric multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present asolutionto one of the main problems of network-centric tracking, namely, decentralized information sharing among the platforms participating in the distributed data fusion. This paper presents a decision mechanism that provides each platform with the required data for the distributed data fusionprocess while reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov decision processes and decentralized lookup substrate, will control the informationexchange process based, among the other parameters, on tracking performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Sirmulation examples demonstrate the operation and the performance results of the system.