Local Posteriors Research Articles

Asynchronous multi-rate multi-sensor fusion based on random finite set

Abstract This paper considers the asynchronous sensor fusion problem for an arbitrary number of sensors with different sampling rates in the framework of the random finite set (RFS) theory. By sequentially predicting and updating the posteriors with measurements according to the arrival time sequence of measurement sets, a centralized asynchronous fusion algorithm, centralized Sequential Processing (SP), is proposed first. It is optimal due to the usage of original measurement information. Considering the reliability, survivability and communication bandwidth as well as flexibility of output time, two distributed asynchronous fusion algorithms are also proposed by assuming that the process of tracking among sensors is independent. The first distributed asynchronous fusion algorithm, namely Batch Generalization Covariance Intersection (B-GCI), utilizes all the predicted local posteriors (LPs) from different sensors and fuses them simultaneously at the fusion center (FC) based on GCI rule, which avoids the complicated calculations of cross-covariance among the LPs of sensors. Considering the large computational burden of the B-GCI fusion algorithm, another one method, Sequential GCI (S-GCI) fusion algorithm, is proposed. The method sequentially fuses the predicted LPs in pairs based on GCI rule at the FC. Performance of proposed three algorithms, including a centralized fusion algorithm and two distributed fusion algorithms, is realized by using Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter and some numerical simulations are given.