Measurement Combining and Progressive Reconstruction in Compressive Sensing
Compressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can be substantial, and the decode cost for all the variables involved can be large. In this paper the authors propose a method to combine measurements from distributed sensors. With their method they can transport and store a single combined measurement set, rather than multiple sets for all sensors.