Multi-sensor distributed fusion based on integrated probabilistic data association
Eui-Hyuk Lee, Darko Mušicki, Taek Lyul Song · International Conference on Information Fusion · 2014
This paper presents two multi-sensor fusion algorithms for single target tracking in cluttered environments. We establish a track quality measure for fusion tracks in centralized fusion and track-to-track fusion. The fusion algorithms are derived by extending the integrated probabilistic data association (IPDA) technique to multisensor systems. We propose a centralized fusion algorithm called the Multi-Sensor IPDA (MS-IPDA). The Multi-Sensor Distributed track-to-track Fusion IPDA (MSDF-IPDA) filter algorithm is also proposed for distributed sensor systems. Both algorithms recursively update the probability of target existence which may be used for false track discrimination.