MIMO Radar Imaging Algorithms

MIMO (Multi-Input Multi-Output) radar is an emerging technology that has drawn considerable attention. With judiciously designed antenna arrays and diversities, MIMO radar can obtain various channels and degrees of freedom, which are much more than the actual antennas. Through joint signal processing of the received data from multiple channels, MIMO radar has been shown to provide enhanced performance in detection, estimation, imaging and etc., so MIMO radar has significant potential for advancing the state-of-the-art of modern radar. Aimed at achieving the imaging of airborne (or spatial) targets, MIMO radar imaging algorithms are studied in this dissertation. The main contents fall into four sections, which are the elements of MIMO radar imaging, back projection (BP) algorithm for MIMO radar imaging, range migration (RM) algorithm for MIMO radar imaging, sidelobe suppression and super-resolution algorithms for MIMO radar imaging.The premise of research on MIMO radar imaging algorithm is to solve some basic problems of MIMO radar imaging, such as data acquisition, design of antenna array, array characteristic, and so on. Therefore, the data acquisition of MIMO radar imaging is analyzed in the first section. Next, the antenna array of MIMO radar imaging is discussed, and then the sampling abilities of MIMO radar array, synthetic aperture array and real aperture array are analyzed contrastively. Their resolution capabilities are discussed respectively and the resolution expression of MIMO radar array is demonstrated. At last, an experiment system for MIMO radar imaging is constructed. The excellent imaging performance of MIMO radar is validated through measured data processing and analysis.Many popular imaging algorithms are not applicable to MIMO radar because of its complicated multistatic architecture, so there is urgent need to explore suitable MIMO radar imaging algorithms. Based on conventional BP algorithm, MIMO radar standard BP algorithm is studied firstly in the second section. This algorithm is not restricted by the array architecture of MIMO radar. Through time-delay curve correction of MIMO radar range compressed data, a novel imaging algorithm called TCC-BP is proposed for MIMO radar. TCC-BP algorithm provides a significant reduction in computational burden. Combing conventional range Doppler (RD) algorithm and BP algorithm, RD-BP algorithm is proposed for MIMO radar imaging. Compared with the standard BP algorithm and TCC-BP algorithm, RD-BP algorithm dramatically improves the processing efficiency while maintaining the imaging quality. Finally, the effectiveness of the proposed imaging algorithms is demonstrated using the measured data by MIMO radar experiment system. MIMO radar spectral imaging algorithm is studied based on array design and RM algorithm in the third section. Firstly, the relationship between radar imaging and spectral filling is discussed, and MIMO radar SF-RM algorithm is proposed. Then MIMO radar imaging performance is evaluated according to the distribution of spectral support area. Based on phase center approximation and a new design of MIMO radar antenna array, UELA-RM algorithm is proposed for MIMO radar imaging, which can conveniently produce images after the correction of displaced phase center error. With orthogonal linear arrays, MIMO radar OLA-RM algorithm is proposed. This algorithm can provide two-dimensional image profile in both azimuth directions via a narrowband MIMO radar system.MIMO radar BP algorithm and RM algorithm are both for the image reconstruction. They can not overcome the inherent sidelobe artifacts and resolution limitation of MIMO radar imaging system. For the purpose of enhancing the quality of MIMO radar images, sidelobe reduction method and super-resolution imaging algorithm for MIMO radar are studied in the last section. Based on the reshaping of spatial spectrum, a novel algorithm called SRSR is proposed. SRSR can effectively reduce sidelobe artifacts without degrading the imaging resolution. The extrapolation of spatial spectrum due to sidelobe reduction is analyzed. Then a super-resolution algorithm in conjunction with SRSR is proposed for MIMO radar imaging. This super-resolution algorithm is called Super-SRSR, which has features of simple process, low computational load, non-sensibility to noise and nonparametric model. In order to solve the problem of huge sidelobes arising from gapped spatial spectrum of MIMO radar, a huge sidelobe reduction algorithm for MIMO radar is proposed. The proposed algorithm that uses autoregressive (AR) based spectral estimation technique is able to improve the quality of MIMO radar images.