Bi-and-multi-static Synthetic Aperture Radars Imaging Technology Research Based On Compressed Sensing

Bi-and-multi-static synthetic aperture radars (SAR) whose receiver and transmitterare located in different platforms have more flexible space configuration and broaderrange of applications. However, they are faced with high-speed A/D data acquisition, alarge amount of data storage as well as wide swath and high resolution contradictionand other problems, which are the key research topics in SAR processing.This dissertation launchs an in-depth study of bi-and-multi-static SAR imagingbased on compressed sensing (CS). Firstly, the range history of bi-and-multi-static SARis analyzed reasonedly. Based on CS, the bistatic SAR and multi-input multi-outputSAR (MIMO-SAR) imaging modes are builded. The random noise bistatic radarimaging technology, broadband synthesis technology and spatial filtering to removeazimuth ambiguties technology are studied to propose a novel bistatic SAR imagingalgorithm and a novel MIMO-SAR imaging algorithm based on CS. This dissertationmainly focuses on the following aspects of work:1. A novel bistatic SAR imaging algorithm based on CS is proposed. The bistaticSAR signal model and the bistatic SAR echo data sparse acquisition method are studiedin detail. The measurement matrixes of CS are constructed based on echo signals. Thisalgorithm reconstructs the targets in range dimension and azimuth dimension by CSmethod, respectively. The numerical simulation results show that this method can getbetter focusing performance.2. A random noise bistatic SAR imaging algorithm is proposed based on CS.Starting from the mathematical description of the random noise signal, the random noisebistatic SAR imaging model is builded and the imaging algorithm is proposed based onCS. The numerical simulations are used to verify the effectiveness of the algorithm andto contrast the effect differences of the traditional narrow band noise imaging and CSimaging.3. A novel compact MIMO-SAR imaging algorithm is prosposed based on CS. Thecompact MIMO-SAR imaging model is builded based on LFM-OFDM signal. Theprinciple of equivalent phase center and broadband synthesis technology are used to achieve compact MIMO-SAR sparse targets imaging. The simulateion results based onpoint targets and real scene verify the effectiveness of the algorithm.4. A novel WAN-MIMO-SAR imaging algorithm is proposed based on CS. Thetechnology of spatial filtering to remove azimuth ambiguties is utilized to achieveWAN-MIMO-SAR sparse targets imaging based on CS. The numerical simulations areused to verify the effectiveness of the algorithm and show that there are manyadvantages among which include reduced on-board storage constraints, higherresolution, lower integrated side-lobe ratio (ISLR) and peak side-lobe ratio (PSLR), lesssampled data than the traditional SAR imaging algorithm, and also indicate that it hashigh robustness and strong immunity in the presence of serious noise.