Information Fusion Filter for Multi-rate Multi-sensor Systems

Abstract

This paper is concerned with the information fusion filtering problem for a class of multi-rate multi-sensor systems, where the system is described at the highest sampling rate and different sensors may have different lower sampling rates. Firstly, the local filters (LFs) at state updating points are proposed by using the LFs at measurement sampling points. Then, the distributed suboptimal fusion filter is obtained by the wellknown covariance intersection fusion (CIF) algorithm. The filtering error variance matrices are derived to obtain the fusion weights. The computational cost is reduced since the cross-covariance matrices between any two local filters are avoided. Simulation example verifies the correctness and feasibility of the proposed algorithm.