Estimation and fusion for tracking over long-haul links using artificial neural networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics - such as the position - is expected to possess much improved quality. In this work, we pursue learning-based approaches for estimation and fusion of target states in long-haul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). The joint effect of 1) imperfect communication condition, namely, link-level loss and delay, and 2) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.