Application in compressed sensing ISAR imaging based on sparse banded measurement matrices

The application of compressed sensing(CS) theory to inversed synthetic aperture radar(ISAR) can make effective use of the data of defective radar echo, and solve the lower quality of imaging, which is caused by the defective data. By analyzing the present situation, it was found that the number of independent random elements in the commonly used Gauss and Bernoulli random measurement matrices was too huge, and the storage space was too large, which led to the high hardware implementation cost. Sparse banded measurement matrices were constructed in this paper, which significantly reduced the number of nonzero elements in measurement matrix and the requirement of system sampling, and saved the hardware implementation cost, by sparsifying measurement matrices banded cyclic shift zero. Finally, the data of simulation and anechoic chamber experiment verify the feasibility and effectiveness of the application of sparse banded measurement matrices into ISAR imaging through the point target model.