Sparsity and Compressive Sensing for SAR signal

Synthetic Aperture Radar (SAR) signal model is considered as a series of echo signals in range direction. The procedure of Principal Component Analysis (PCA) is introduced which is used as transformation basis to sparsify the SAR signals. The joint Compressive Sensing (CS) and PCA algorithm is derived to realize SAR raw data sparse and compressive measurement. The numerical simulation results demonstrate that the PCA method has good sparse performance and the joint CS and PCA algorithm is possible to online compressive measure the SAR raw data.