Information Flow Control for Collaborative Distributed Data Fusion and Multisensor Multitarget Tracking

Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms, is presented. A decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system is presented as well. A distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable is considered. In this study, the approach to use an information-based objective function is utilized. The objective function is based on the posterior Cramér–Rao lower bound and constitutes the basis of a reward structure for Markov decision processes that are used to control the data-fusion process. Three distributed data-fusion algorithms—associated measurement fusion, tracklet fusion, and track-to-track fusion—are analyzed. This paper also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.