Compressed Sensing Based Clutter Suppression For MIMO Radar

With the complicated electromagnetic environment, high radar performance is greatly demanded and Multiple-input multiple-output(MIMO) Radar has been drawn more and more attention for researchers. Compared with traditional phased array radar, the transmit elements of MIMO radar transmit mutually orthogonal waveforms, and transmit channels can be separate from the received signals by pulse compression processing in receiver. the degree of freedom is increased, and the radar performance has been improved. The combination of bistatic radar and MIMO technology can not only have the advantages of MIMO radar, but also bring the advantages of covert operation, reduced susceptibility to jamming in bistatic radar. What’s more, Bistatic MIMO radar has the particular advantage of being able to obtain the transmit angle information by processing the received data, thereby reduces the time synchronization requirement in the traditional configuration of radar. In this thesis, the new technology-Compressed Sensing theory is applied to the MIMO radar. Simultaneously, this thesis also considers two issues in Bistatic MIMO radars, including Clutter Suppression, the influence elimination of undesirable transmitted waveform for the detection performance, and space-time adaptive processing(STAP). The main contributions of this thesis are summarized as follows:1. In practice, it is difficult to design waveform sets which have ideal autocorrelation property as well as cross-correlation one. The non-ideal waveform set will result in deviation of the estimated clutter covariance matrix, thus degrade the performance of the radar seriously. In order to solve this problem, this thesis analyzes why autocorrelation and cross-correlation can affect clutter suppression ability firstly. Secondly, a clutter suppression method based on compressed sensing is proposed. This method not only has a good performance in a small sample, but also can avoid the impact of undesirable transmitted waveform for clutter suppression effectively. Simulation results demonstrate the effectiveness of the proposed algorithm.2. Due to the virtual aperture of MIMO radar by the transmitted waveform diversity, degree of freedom(Do F) and the system performance of MIMO radar are enhanced. Although it has many advantages, it can also bring the increased dimensions of theclutter covariance matrix(CCM) and requirement of independent and identically distributed(i.i.d) training samples. By combining bistatic MIMO radar and CS-STAP technology, it is possible to reduce the requirement for the number of samples, and eliminate the discrete clutter interference in the test cell. However, it will increase the amount of calculation greatly, and be hard to apply in engineering. Therefore a dimension reduced compressed sensing(DRCS) method is proposed in this thesis. Compared with direct data domain using spares representation(D3SR) method, DRCS need less number of samples and can overcome the shortcoming of computationally intensive. Simulation results demonstrate the effectiveness of the proposed algorithm.