Heterogeneous sensor fusion based on Copula theory and importance sampling

Abstract

This study deals with the problem of heterogeneous sensor fusion based on Copula theory and importance sampling. The proposed fusion algorithm is grounded on Copula statistical modelling and Bayesian probabilistic theory. The distinctive advantage of this Copula-based methodology is that it formulates the internal dependency between the local sensors' data, which is usually unknown but essential for accurate track fusion. To this end, a joint distribution of the local sensors' observations is constructed based on Copula functions, and the corresponding fusion rule is derived with a specific correlation term. In addition, a Monte-Carlo importance sampling technique is adopted to improve the computational efficiency by drawing less random samples from the local estimates to be fused. After that, a procedure of Kernel density estimation is applied to learn a Gaussian approximation of the fused density. In the end, extensive Monte-Carlo simulations are conducted to evaluate the proposed sensor fusion method in a distributed target-tracking scenario.