Raw data compress method of Synthetic Aperture Radar based on compressive sensing

Nowadays, due to the need of high resolution imaging, huge amount of data are needed to be collected base on Nyquist sampling theorem. However, the acquisition platform cannot afford the computation requirement to process on board, so those data must be sent to the ground so that it can be processed. This paper is focused on the compression of the Synthetic Aperture Radar (SAR) raw data to send as less data as we can ease the burden on the system and reduce the time for transmission. In this paper, we compressed the SAR raw data using compressive sensing method, and we train the sparse basis through K-SVD method. First we use the raw data that we collected to train the sparse basis using K-SVD method, when we get the trained sparse basis, we only need to send part of the raw data with the basis to the ground and the raw data can be recovered perfectly. The result of recovered data and imaging results are given. This method can help us to perform further application research of SAR imaging.