Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors

Abstract

We propose the information regularization principle for fusing information from sets of identical sensors observing a target phenomenon. The principle basically proposes an importance-weighting scheme for each sensor measurement based on the mutual information based pairwise statistical similarity matrix between sensors. The principle is applied to maximum likelihood estimation and particle filter based state estimation. A demonstration of the proposed regularization scheme in centralized data fusion of dense motion detector networks for target tracking is provided. Simulations confirm that the introduction of information regularization significantly improves localization accuracy of both maximum likelihood and particle filter approaches compared to their baseline implementations. Outlier detection and sensor failure detection capabilities, as well as possible extensions of the principle to decentralized sensor fusion with communication constraints are briefly discussed.

Audio-Visual Group Recognition Using Diffusion Maps

“…The nonparametric probabilities were computed by Parzen windows. Ozertem and Erdogmus [11] used MI to compute pairwise affinities between sensors and thus derive an importance-weighting scheme for each sensor. The scheme is shown to improve the performance of a particle filter-based motion tracker.…”