基于压缩感知的MIMO雷达杂波抑制方法

发布时间：2019-06-20 22:53

【摘要】：随着电磁环境的复杂化和人们对雷达性能要求的提高,多输入多输出(MIMO)雷达越来越受到人们关注。与传统的相控阵雷达相比,MIMO雷达发射机的每个阵元发射相互正交的波形信号,在接收端通过对回波信号作脉冲压缩处理来分离发射通道,从而增加了自由度,使雷达性能得到了提升。将MIMO技术引入机载双基地雷达,不但具有上述MIMO雷达的优势,还具有双基地带来的探测隐身目标、隐蔽侦查等优势。该系统通过对接收机的回波数据进行处理,得到了发射机与接收机相对于目标的角度信息,从而降低了传统配置下雷达对时间同步性的要求。本文将近几年产生的新技术--压缩感知(CS)理论应用到MIMO雷达,并针对双基地MIMO雷达的杂波抑制、消除MIMO雷达发射波形不理想对检测性能产生的影响以及空时自适应处理(STAP)进行了研究。主要工作为如下两个方面:1、在实际中,难以设计具有理想相关性的正交波形,而波形的自相关与互相关旁瓣不为零将会导致杂波协方差矩阵的估计造成误差,进而对雷达性能产生了严重的影响。针对这一问题,本文首先分析了自相关和互相关对杂波抑制产生影响的原因,其次提出了一种基于压缩感知的杂波抑制方法。该方法不但在小样本下具有良好的性能,而且可以有效的消除由于发射波形不理想对杂波抑制造成的影响。通过对所提方法的性能进行仿真分析,验证了该算法能够有效的消除发射波形不理想给MIMO雷达杂波抑制所带来的影响。2、由于MIMO雷达通过发射波形分集来增加虚拟孔径,使MIMO雷达系统自由度增加,进而提高了MIMO雷达的系统性能。其自由度增加虽然带来诸多优点,但是也会造成杂波协方差矩阵的维数增加和独立同分布的样本需求增加。将CS-STAP引入双基地MIMO模型时,虽然可以降低对样本数的需求,并消除检测单元离散杂波点的干扰,但是其计算量将大大增加,不利于实现。因而本文针对这一问题提出了一种基于压缩感知的降维杂波抑制方法,使其既拥有直接数据域的压缩感知方法所具备的样本数较少的优点外,又改善了其计算量大的不足。通过对结果的仿真分析,可以证明在性能损失不多和小样本的情况下,其计算量将大大减少。
[Abstract]:With the complexity of electromagnetic environment and the improvement of radar performance, multi-input and multi-output (MIMO) radar has attracted more and more attention. Compared with the traditional phased array radar, each element of MIMO radar transmitter transmits orthogonal waveform signals, and the transmitting channel is separated by pulse compression of echo signal at the receiving end, thus increasing the degree of freedom and improving the radar performance. The introduction of MIMO technology into airborne bistatic radar not only has the advantages of the above MIMO radar, but also has the advantages of bistatic detection of stealth targets, covert detection and so on. By processing the echo data of the receiver, the angle information of the transmitter and the receiver relative to the target is obtained, which reduces the requirement of time synchronization of the radar under the traditional configuration. In this paper, the compression sensing (CS) theory, which is a new technology produced in recent years, is applied to MIMO radar, and the clutter suppression of bistatic MIMO radar, the elimination of the influence of unsatisfactory transmission waveform of MIMO radar on the detection performance and the adaptive processing of (STAP) are studied. The main work is as follows: 1. In practice, it is difficult to design orthogonal waveforms with ideal correlation, and the autocorrelation and cross-correlation sidelobe of waveforms are not zero, which will lead to errors in the estimation of clutter covariance matrix, which will have a serious impact on radar performance. In order to solve this problem, this paper first analyzes the causes of the influence of autocorrelation and cross-correlation on Clutter suppression, and then proposes a method of Clutter suppression based on compressed perception. This method not only has good performance in small samples, but also can effectively eliminate the influence of transmitting waveform on clutter suppression. Through the simulation and analysis of the performance of the proposed method, it is verified that the algorithm can effectively eliminate the influence of the unideal transmission waveform on the clutter suppression of MIMO radar. 2, because MIMO radar increases the virtual aperture by transmitting waveform diversity, the degree of freedom of MIMO radar system is increased, and then the system performance of MIMO radar is improved. Although the increase of degree of freedom brings many advantages, it will also lead to the increase of the dimension of the clutter covariance matrix and the increase of the demand for independent and homogeneously distributed samples. When CS-STAP is introduced into the bistatic MIMO model, although the demand for the number of samples can be reduced and the interference of discrete stray points in the detection unit can be eliminated, the amount of computation will be greatly increased, which is not conducive to the implementation. Therefore, in order to solve this problem, a reduced dimension clutter suppression method based on compressed sensing is proposed in this paper, which not only has the advantages of less samples in direct data domain compression sensing method, but also improves the shortage of large amount of computation. Through the simulation analysis of the results, it can be proved that the calculation amount will be greatly reduced when the performance loss is small and the sample is small.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51

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