This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena su...
Juan Liu, Maurice Chu, Jie Liu, Jim Reich, Feng Zh...
Abstract— This paper considers the problem of tracking objects with sparsely located binary sensors. Tracking with a sensor network is a challenging task due to the inaccuracy of...
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
— With the deployment of large, distributed networks of cameras and other sensors, it is becoming necessary to also address the issue of how to effectively present the large vol...
In this paper we study a dynamic sensor selection method for Bayesian filtering problems. In particular we consider the distributed Bayesian Filtering strategy given in [1] and sh...