Information Regularized Maximum Likelihood for Binary Motion Sensors

We propose a pairwise mutual information based regularization technique for maximum likelihood sensor fusion in dense distributed sensor networks. The principle is demonstrated in target localization and tracking using a dense binary motion sensor network under a centralized data fusion framework. Simulations demonstrate that the information regularization enables the maximum likelihood localization procedure to provide significantly more accurate target position estimates compared to its unregularized counterpart, which is the current benchmark. The extensions of the information regularization principle to various sensor and data fusion problems such as outlier detection, and sensor failure identification are discussed.