Compression and Reconstruction of Chirp Echo of Broadband Imaging Radar Based on Compressed Sensing

Under Band-Pass sampling theorem, the chirp echo of broadband imaging radar brings large data quantity and it is hard to store. In order to solve this problem, this paper put forward the compression and reconstruction method under the compressed sensing theory. Firstly, the sparseness of echo is researched using chirplet transform for whether the echo is compressible. Secondly, under the sparseness of the echo, the practicable sparsity dictionary and its compact form in compressed sensing is given based on chirplet transform and the qualification of sparse dictionary in compact form for recovery is proved. In the end, the compression and reconstruction method for the echo is given and the numerical experiment under the inverse synthetically aperture radar (ISAR) is done. In the model of rotation with movement, the data is compressed and reconstructed. From the comparison of time-domain waveform, high resolution range profile (HRRP) and ISAR image between original and the recovered data, the efficiency of compressing method is validated. In the end, the curve of error for reconstruction corresponding to signal-noise-ratio (SNR) is analyzed and the demand of the method put forward in this paper to SNR is shown.