Bistatic ISAR Imaging Algorithm Based On Compressed Sensing

Compared with traditional inverse synthetic aperture radar(ISAR), Bistatic ISAR has larger radar range and better security. Because of the transmit and receive radar placed in different location, it’s perform better anti-interference and anti-interception. Bistatic ISAR is of great importance in military and civilian fields. However, it is precisely because of the transmit and receive radar are in different location, and bistatic included angle exists, the imaging resolution of Bistatic ISAR is lower than traditional ISAR. And more, the targets of Bistatic ISAR are usually non-cooperative, without known regular move, so, affected by actual situation, the received echo signal may be partially missing. In this situation, traditional signal sampling method can no longer helps to achieve good imaging results. Increasing transmitting signal bandwidth is increased by traditional solutions usually to raise range resolution. And linear interpolation and all-pole model matching algorithm in sparse part of signal are utilized to help to get higher resolution imaging result. Nevertheless, when the bistatic included angle is too large,or the sparse degree of the signal is too large, this kind of methods will not work any more. Compressed sensing(CS) theory point out that there is a optimize method can be used to get high resolution reconstruction result under the condition of much lower sampling rate than Nyquist sampling rate was used. In this paper, CS theory is used in Bistatic ISAR imaging algorithm to solve the low resolution problem caused by large bistatic included angle and sparse aperture condition. the dissertation launches an analytical research around the related theory and reconstruction algorithms from the following several aspects:(1) Raise CS theory in terms of the drawbacks of traditional Nyquist sampling theory. Focus on the three key steps of CS theory: sparse representation of echo, the structure of the observation matrix, and the reconstruction algorithm. Lay the groundwork for the Bistatic ISAR imaging algorithm based on CS.(2) Introduce traditional imaging algorithm of Bistatic ISAR, Mainly introduce the range doppler(RD) algorithm. Fucose on the analysis of bistatic included angle of size effect on the imaging range resolution and azimuth resolution, and the influence of sparse aperture of image quality, to raised out the combining of CS theory and Bistatic ISAR imaging system.(3) Combining with CS and Bistatic ISAR. First analysis the turntable imaging model and use stepped frequency signals as transmit signal to get echo. Build sparse basis matrix on the basis of the echo. Then create the suitable observation matrix to get lower dimensional observation samples. At last, use suitable reconstruct algorithm to reconstruct the original signal from those samples. On this reconstruction issue, research the Norm minimization algorithm includes Basis Pursuit(BP) algorithm and Gradient projection for sparse reconstruction(GPSR) which based on convex optimization,Orthogonal Match Pursuit(OMP) algorithm and GAPES algorithm which based on greedy algorithm. Finally, simulation experiments on the effectiveness of the various imaging algorithm is verified and analyzed.(4) Through the simulation experiment to compare the reconstruction error, location estimation precision, signal sparse degree and antinoise performance of various reconstruction algorithm is analyzed.