Compressed Sensing in Soil Ultra-Wideband Signals

This paper investigated the compressed sensing (CS) of ultra-wideband (UWB) soil echo signals. When CS is used in the transmission of UWB signals, sampling rate can be effectively reduced and sparse signals can be reconstructed from fewer observations. Therefore, how to apply CS into UWB soil echo signals is of great importance. The proposed approach reveals that UWB signals can be expressed by linear combinations of many atoms from a proper dictionary. In this paper, K-singular value decomposition (KSVD) dictionary and three types of Gaussian pulse dictionaries are designed, and the probability of successful reconstruction can reach 0.95. It is shown that Gaussian first-order derivative dictionary is the most suitable; the root-mean-square error (RMSE) of UWB signals and reconstructing signals is lower than 0.12.