Rao-Blackwellized unscented Kalman filter for nonlinear systems with bandwidth constraints

We consider the state estimation problem in distributed nonlinear systems with bandwidth constraints. In particular, we focus on the sensor networks with limited communication between the sensor nodes and the fusion center. Two practical bandwidth-saving methods are considered: (1) recursive filtering with quantized innovations, and (2) compressive sampling of sparse signals. For both scenarios, Rao-Blackwellized unscented Kalman filter (RBUKF) based methods are developed. The simulation results demonstrate that the proposed algorithms closely track the original signal.