Abstract
Motivated by recent advances on Compressive Sensing (CS) and high data redundancy among radars in radar sensor networks, we study CS for radar sensor networks. We demonstrate that the sense-through- foliage UWB radar signals are very sparse, which means CS could be applied to radar sensor networks to tremendously reduce the sampling rate. We propose to apply SVD-QR and maximum likelihood algorithms to CS for radar sensor networks. SVD-QR could vastly reduce the number of radar sensors, and CS is applied to the selected radar sensors for data compression. Simulations are performed and our compression ratio could be 192:1 overall.
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