Application Of Compression - Aware Reconstruction Algorithm In Wideband Radar Signal Processing

Radar has been widely used in military and civil field. The emergence of wideband radar signals have brought challenge to the radar signal processing system, according to the Nyquist sampling theorem, the wideband radar signal after sampling will get a large amount of sampling data, large amount of data will bring pressure to the signal processing system. On the premise of signal is sparse or compressible, compressed sensing theory compresse the data appropriately while sampling the original signal. And it can realize the precise or approximate reconstruction by a small number of signals observed value. In this paper, the compression perception reconstruction algorithm and its application in radar signal processing are studied deeply, the main work is as follows:(1) Studied the sparse dictionary. In this paper, the compressed sensing theory will be used in radar signal receiving process under Gabor dictionary and matched dictionary respectively.For the shortcomings of low precision and large computation. It is proposed to make up for the shortcomings through improving the reconstruction algorithm due to selecting the Gabor dictionary(2) Compressed sensing reconstruction algorithm is studied deeply. In view of the traditional greedy algorithm’s shortcomings in the way of choosing atoms, it is easily trapped in local optimal. ICAOMP algorithm and A*OMP algorithm are proposed while improving the A*OMP algorithm. The experimental results show that ICAOMP algorithm, A*OMP algorithm and its improved algorithm have a better performance.(3) Studied the compressed sensing under Gabor dictionary and matched dictionary in the application of wideband radar echo signal of target detection and one dimensional range imaging. Firstly, when the Gabor dictionary as sparse dictionary, studied the reconstruction performance of ICAOMP algorithm, A*OMP algorithm and OMP algorithm while comparing the three algorithms respectively in quiet and noisy environment. Then, when the matching dictionary as sparse dictionary, studied the reconstruction of signal respectively in quiet and noisy environment. Finally, the reconstruction performance of the compressed sensing under the Gabor dictionary and the compressed sensing under the matched dictionary will be analysed and compared respectively in quiet and noisy environment.