Asynchronous Track-to-Track Fusion Algorithms for Distributed Multi-sensor System

This paper derives an asynchronous track-to-track fusion algorithm that combines tracks provided by different sensors that have different sampling communication delay. In this algorithm, an adaptive approach for track-to-track fusion in a multi-sensors environment is used. The measurements of two sensors tracking the same target are processed by linear Kalman Filters, and the outputs of the local trackers are sent to the central node. In this node, a decision logic, which is based on the comparison between distance metrics and thresholds, selects the method to obtain the global estimate. The simulation result illustrates that this algorithms approaches the Weighted Covariance Fusion (WCF) algorithm in the fusion precision, and the computational burden reduces one about the half.