Distributed sensor fusion for RFS density with consideration of limited sensing ability

The paper addresses the problem of distributed sensor fusion in the framework of random finite set. The Generalized Covariance Intersection (GCI) rule of multi-target densities is extensively used in multi-target Bayesian filtering scheme. But there are two problems in GCI which are unreasonable design of fusion weight and unable to tackle informative differentiation. In order to get rid of the bad influence of these two problems, we propose a heuristic Heuristic distributed fusion (HDF) method by two steps: fusion weight reconstruction and information difference preservation. Finally, we compare the GCI fusion with our proposed HDF method in two scenarios. The results show that HDF is more robust and can achieve better performance.