Study On Compressive Sensing In Radar Signal Processing

High-resolution radar imaging system is widely used in military and civil fields, Wide-band radar signals are needed in high-resolution radar imaging, According to the sampling theorem, wide-band signals result in high-sampling rate, high-transmission rate, large data storage in the radar system.Compressive sensing(CS) takes samples from the information of the signal,acquires measured values by relevant measurement matrix,and reconstructs signals by optimization theory..The theory of pulse compression, compressive sensing and the application of CS into one-dimension range-profile are studied in this dissertation. Firstly the CS theory, including the signal sparsity, the noncorrelation measurement matrix and the accurate reconstruction of the signal is introduced, The analog-to-inform-ation converter (AIC) is given out and applied into the radar receiver to reduce the sampling frequency.Then, the pulse compression theory is introduced. Matched filtering and de-chip processing are studied in detaile. The pulse compression theory is the foundation of the sparse decomposition of the radar signal.Following the study of the pulse compression theory and CS, the sparse representation models of both matched filtering and de-chip processing are established, An algorithm of one-dimension range-profile imaging, which can preserve the phase information, is proposed. Simulations show that the proposed algorithm can reconstruct one-dimension range-profile imaging accurately with a much lower sampling frequency, and can reduce the shielding of weak signals by the side lobe of strong signals.