Study On Compressed Sensing Application To Radar Signal Processing Based On AIC

In the field of modern communication and radar systems, broadband signal has more and more application, as for the radar system, whether military or civilian, all need high range resolution, and range resolution is directly determined by the signal bandwidth, so that the bandwidth of radar system launch signal has been more and more wide, and in the processing of receiving, as a result of the limitation of Nyquist sampling theorem, the echo signal sampling rate is greater than 2 times the bandwidth of signal, it makes the hardware implementation more and more difficult. Considering the above problems, we hope to break the Nyquist specific restrictions, and find a method not depending on signal bandwidth to deal with the echo signal, and compressed sensing put forward for this. Compressed sensing theory is no longer limited to the bandwidth of the signal when sampling but is decided by the structure and contents of the signal, so this can largely reduce resource consumption and improve performance.Compressed sensing is a new direction between mathematics and information science popular in recent years, and it has brought a revolutionary breakthrough for the signal collection technology, and it adopts the adaptive linear projection to preserve the original structure of the signal, can use a few observations to extract the useful information in the signal and use far below the Nyquist frequency to sample the signal, and reconstruct the original signal through numerical optimization problem accurately. So this thesis will apply compressed perception theory to process radar echo signal, so that it can improve the performance of radar system.In this thesis, the main research contents is that the continuous radar echo signal in analog domain is directly compressed sampling to get discrete sample point by analog information conversion structure, and next, use reconstruction algorithm to get sparse coefficient, and the sparse coefficient is the matched filter output by the matched filtering algorithm, and get distance item to the compression through the phase information and target scattering coefficient information, and lastly, use remained phase information to compressed direction so as to get wave direction. This not only can greatly reduce the transport cost, but also the matched filtering algorithm based on compressed sensing can overcome the disadvantage that traditional matched filtering algorithm in low SNR has sidelobe interference, and just matched filter output, the noise is small, so it greatly improve the performance of the whole system. The combination compressed sensing algorithm makes the range resolution of the radar system reduced, so the last also introduces a model of cooperative MIMO radar transceiver based on compressed sensing. This model is able to quickly gather the echo signal of the data all the direction, and can overcome the disadvantage that the distance item to the compression of echo signal has low resolution.