Estimating Time-Varying Sparse Signals Under Communication Constraints
In this correspondence, we consider reconstruction of time-varying sparse signals in a sensor network with communication constraints. In each time interval, the fusion center transmits the predicted signal estimate and its corresponding error covariance to a selected subset of sensors. The selected sensors compute quantized innovations and transmit them to the fusion center. We present algorithms for sparse signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.