Fuzzy Consensus Cubature Information Filtering for Space Target Tracking

Abstract

A fuzzy distributed nonlinear state estimation issue on the possibilistic framework is investigated in this paper. Firstly, unlike the Gaussian probability distributions on the traditional probability framework, the noises are modeled as fuzzy random variables (FRVs) with trapezoidal possibility distributions (TPDs), and then a fuzzy cubature paradigm is proposed. Secondly, based on the fuzzy cubature paradigm, a novel fuzzy consensus cubature information filtering (FCCIF) algorithm is proposed for sensor networks wherein each agent fuses local fuzzy information with fuzzy information from the neighbors based on the weighted average consensus. Furthermore, under well‐known observability and connectivity assumptions, it is proved that the FCCIF possesses guaranteed stability regardless of consensus steps. Finally, a space target tracking simulation is developed to verify the validity of the FCCIF algorithm. The FCCIF algorithm can be used in navigation guidance, radar tracking, sonar ranging, satellite orbit determination, and other practical applications when the noises are suitable for qualitative rather than quantitative description.