Study On The Appilcation Of Compressive Sensing In Radar Imaging

High-resolution radar imaging is widely demanded in many applications. Usually high-resolution radar imaging needs wide-band radar signals, and wide-band radar signals result in the increase of data rate. In recent years, compressive sensing (CS) theory is highly focused in radar community, and its incoherence measurement process can effectively reduce the data rate of high-resolution imaging radar system, and release the burden of radar system on huge amount of data sampling, storage and transmission. So, CS theory and technologies may bring deep change to high-resolution imaging radar system. Although the research of CS based radar imaging has made some progress, there is still lack of systemic research on the CS based radar imaging theory, and no practicable imaging algorithm. In the dissertation, the theory and algorithms of CS based radar imaging is discussed and applied to high-resolution radar imaging. The major works include the following three parts: the CS based radar imaging data acquisition methods, the CS based radar imaging algorithms and the application of CS in wide-band radar imaging. Firstly, we establish the sparse representation models of the baseband echo under both matched filtering and de-chirp processing, and propose digital or analog realization scheme of analog-to-information convertor in radar receiver. Secondly, we realize a phase-reservation CS based range compression algorithm, constructe a CS based radar imaging framework with range and azimuth decoupled and apply it to both 2D and 3D radar imaging combined with conventional imaging algorithms. Finally, we apply the CS based imaging method to wideband radar imaging system. The effectiveness of the proposed algorithms are tested through processing both simulation and real data.The major contributions of the dissertation are summarized as follows:In the study of CS based radar data acquisition methods, we firstly analyze the radar echo signal, and then establishe the sparse representation models of the processed signals under matching filter mode and de-chirp mode. Aiming to real-time measurement, we introduce Analog-to-Information converter(AIC) into compressive sensing imaging processing, and proposes both digital and analog solutions of AIC in radar receiver.In the study of CS based imaging algorithms, we firstly select the sparse signal reconstruction algorithm suitable for radar imaging, and then propose a phase-reserve CS range compression algorithm combined with sparse representation of radar echo signal and non-correlation measurement matrix. Finally, we propose a range-azimuth decoupling radar imaging frame, in which CS range compression algorithm is combined with traditional radar 2D imaging and 3D imaging algorithms so as to realize the CS imaging algorithm.In the study of CS based wide-band radar imaging algorithm, we propose a CS measurement method for stepped-frequency chirp signal (SFCS) and realize CS imaging for wide-band radar with application of the subaperture processing method of SFCS.