Information feedback for estimation and fusion in long-haul sensor networks

Long-haul sensor networks can be found in both civilian and military applications. In a typical long-haul sensor network, sensors are remotely deployed over a large geographical area to perform 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. However, imperfect communication conditions can become the bottleneck for desired estimation and fusion performance. The link-level loss and delay - such as that over a satellite channel - can easily reduce the chance that an estimate is successfully received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimal accuracy performance of the underlying task. In this work, we explore the effect of information feedback in the context of state estimation and fusion in a communication-constrained long-haul sensor network. Different feedback configurations and schedules are proposed. In particular, the joint impact of communication delay/loss, information feedback, and computation constraints is explored by means of analytical and simulation studies.