Multi-sensor track-to-track fusion with target existence in cluttered environments

Abstract

Multi-sensor fusion for multiple target tracking in cluttered environments is needed for improving tracking accuracy and track maintenance over single sensor target tracking in real target tracking applications. A track quality measure, such as the probability of target existence, can be used when fusing local sensor tracks for false track discrimination at the fusion centre and to improve the performance of the track-to-track association. The track quality measure is a highly effective tool for track maintenance and lowers the computational load by sending tracks with a high probability of target existence to the track centre. In this study, the authors utilise the iterative joint-integrated probabilistic data association technique for multi-sensor distributed fusion systems. Track-to-track fusion performance also depends on distributed fusion architecture. The track-to-track fusion algorithm is evaluated in distributed fusion systems without memory, with memory, and with feedback. The track confirmation rates and position errors are simulated and compared with low-level centralised fusion, which is theoretically optimal.