Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 59, Issue 6, Pages 2961-2964Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2011.2128312
Keywords
Compressed sensing; Kalman filter; particle filter; quantized innovations
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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.
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