Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks

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 sensor network without a fusion center. We present an optimal distributed Bayesian estimation algorithm that is sequential both in time and in space (i.e., across sensors) and requires only local communication between neighboring sensors. For the linear/Gaussian case, the algorithm reduces to a time-space-sequential, distributed form of the Kalman filter. We also demonstrate the application of our state estimator to a target tracking problem, using a dynamically defined ldquolocal sensor chainrdquo around the current target position.