Sequential Compressive Target Detection in Wireless Sensor Networks

Compressed sensing is an emerging theory which provides a new framework for sampling and compressing a sparse signal simultaneously at a reduced sampling rate. Besides this, compressed sensing also provides a new approach for the task of detection. Detection from compressive measurements without reconstructing the signals remains as a challenging problem. In this paper, we investigate the performance of compressive detection and propose a sequential compressive detection scheme to reduce the number of measurements for target detection in wireless sensor networks. We derive the sequential compressive decision rules and analyze its detection performance in terms of the number of measurements. Simulations show that sequential compressive detection can save about 50 percents of the average number of measurements under a given detection performance requirement compared with that of compressive detection.