Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport

TL;DR

This paper introduces an entropy-regularized optimal mass transport approach for sensor fusion in source localization, offering robustness to sensor misalignment and calibration errors with efficient computation for high-dimensional problems.

Contribution

It develops a novel convex barycenter formulation incorporating entropy regularization for robust, low-complexity sensor fusion in source localization.

Findings

Robustness to sensor misalignment demonstrated

Efficient algorithm suitable for high-dimensional problems

Numerical validation in 2D localization scenarios

Abstract

This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solution algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method's inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions.