Performance-driven Entropic Information Fusion

Advances in technology have resulted in acquisition and subsequent fusion of data from multiple sensors of possibly different modalities. Fusing data acquired from different sensors occurs near the front end of sensing systems and therefore can become a critical bottleneck. It is therefore crucial to quantify the performance of sensor fusion. Information fusion involves estimating and optimizing an information criterion over a transformation that maps data from from one sensor data to another. It is crucial to the task of fusion to estimate divergence to a high degree of accuracy and to quantify error in the estimate. To this end, we propose a class of plugin estimators based on k-nearest neighbor (k-NN) graphs for estimating divergence. For this class of estimators, we derive a large sample theory for the bias and variance and develop a joint central limit theorem for the distribution of the estimators over the domain of the transformation space. In this paper, we apply our theory to two applications: (i) detection of anomalies in wireless sensor networks and (ii) fusion of hyperspectral images of geographic images using intrinsic dimension.