Categorical soft data fusion via variational Bayesian importance sampling with applications to cooperative search

Abstract

This paper considers Bayesian data fusion with categorical ‘soft sensor’ information, such as human input in cooperative multi-agent search applications. Previous work studied variational Bayesian (VB) hybrid data fusion, which produces optimistic posterior covariance estimates and requires simple Gaussian priors with softmax likelihoods. Here, a new hybrid fusion procedure, known as variational Bayesian importance sampling (VBIS), is introduced to combine the strengths of VB and fast Monte Carlo methods to produce more reliable Gaussian posterior approximations for Gaussian priors and softmax likelihoods. VBIS is then generalized to problems involving complex Gaussian mixture priors and multimodal softmax observation models to obtain reliable Gaussian mixture posterior approximations. The utility and accuracy of the VBIS fusion method is demonstrated on a multitarget search problem for a real cooperative human-automaton team.