Integrated Multi-Modal RF Sensing. Sensor Integration and Joint PDF Construction for Distributed Detection and Classification

摘要

With multiple sensors in distributed systems, one is expected to make better decisions than with a single sensor. We investigate the problem of sensor integration to combine all the available information. In this paper, we propose a novel method of constructing the joint probability density function (PDF) based on the exponential family. This method does not require the knowledge of the marginal PDFs and hence is useful in many practical cases. We prove that our method is asymptotically optimal in Kullback-Leibler (KL) divergence. It is shown that the performance of our method is the same as existing methods, while it requires less information.