Adaptive track fusion in a multisensor environment

The aim of this paper is to derives an adaptive approach for track fusion in a multisensor environment. The measurements of two sensors tracking the same target are processed by linear Kalman filters. 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. Numerical simulations assess the influence of the thresholds and of the sensor noise ratio on the adaptive algorithm's performance. The values of the thresholds govern the tradeoff between accuracy and computational burden. The main advantage of the adaptive fusion is its ability to react to changes in the system characteristics.