基于压缩感知的MIMO雷达角度估计方法研究

发布时间：2018-03-15 03:27

  本文选题：MIMO雷达　切入点：角度估计　出处：《南京航空航天大学》2014年硕士论文　论文类型：学位论文

【摘要】：多输入多输出(Multiple Input Multiple Output,MIMO)雷达一种新体制雷达,其系统的优越性,对深入理解传统意义上的雷达,以及新概念雷达的研究具有指导意义,而压缩感知(Compressed Sensing,CS)理论作为一种新兴的信号处理技术,已经被众多研究者应用在雷达领域的信号处理中。本文将压缩感知理论应用于MIMO雷达目标的角度估计中,主要工作如下:1、MIMO雷达空间谱估计的研究。将传统空间谱估计方法,如Capon,MUSIC以及ESPRIT,推广到双基地的MIMO雷达目标(Direction Of Department,DOD)和(Direction Of Arrival,DOA)的估计中。此外,针对MIMO雷达在色噪声环境中的角度估计问题,引入高阶累积量,提出了一种基于改进四阶累积量的角度估计方法。在保证虚拟阵列孔径有效扩展的前提下去除回波信号中的冗余项,达到矩阵降维的目的,最后利用MUSIC-like算法进行谱峰搜索得到估计值。所提算法能够有效抑制高斯色噪声,在保证估计精度的基础上,能够减少四阶累积量矩阵的维数,计算的复杂度也得到降低。2、将压缩感知理论应用于MIMO雷达目标角度估计的问题中,提出一种基于稀疏重构的MIMO雷达DOD和DOA联合估计方法。首先在二维角度空间中构造冗余字典;进行协方差矩阵的特征分解,从中选取有效的特征向量在该冗余字典下稀疏表示,构建低维稀疏线性模型;最后通过重构算法得到目标的角度信息。该方法对特征向量的稀疏重构降低了重构原始接受信号的计算复杂度,且在低信噪比和低快拍下仍有较好的估计性能。3、将基于高阶累积量的算法与压缩感知理论相结合,提出一种基于高阶累积量与稀疏表示的MIMO雷达收发角度估计方法。首先利用四阶累积量对高斯噪声不敏感的特性对信号进行降噪处理;然后对四阶累积量矩阵进行特征分解得到信号子空间,通过将其中的特征向量在合适的冗余字典上稀疏表示,通过重构算法求解稀疏系数,进而联合估计目标的收发角度。仿真结果表明,所提方法不仅能够有效抑制高斯色噪声,而且具有较高的稳健性。
[Abstract]:Multiple-Input-Multiple-output Input Multiple OutputMimo Radar A new system radar, the superiority of its system is of guiding significance for the deep understanding of the traditional radar and the research of the new concept radar. As a new signal processing technology, compressed sensing theory has been used by many researchers in the field of radar signal processing. In this paper, the compressed sensing theory is applied to the angle estimation of MIMO radar targets. The main work of this paper is as follows: 1) the research on spatial spectrum estimation of MIMO radar is as follows. The traditional methods of spatial spectrum estimation, such as CaponMUSIC and Esprit, are extended to the estimation of bistatic MIMO radar targets (DOD) and Direction of of ArrivalDOAs. To solve the problem of angle estimation of MIMO radar in colored noise environment, a high order cumulant is introduced. An angle estimation method based on the improved fourth order cumulant is proposed. The redundant items in the echo signal are removed under the premise of effective expansion of the virtual array aperture, so as to achieve the purpose of reducing the dimension of the matrix. Finally, the spectral peak search of MUSIC-like algorithm is used to obtain the estimated value. The proposed algorithm can effectively suppress Gao Si color noise and reduce the dimension of the fourth order cumulant matrix on the basis of guaranteeing the estimation accuracy. The computational complexity is also reduced. The compressed sensing theory is applied to the problem of MIMO radar target angle estimation. A sparse reconstruction based joint estimation method for MIMO radar DOD and DOA is proposed. Firstly, redundant dictionaries are constructed in two-dimensional angle space. The eigenvalues of the covariance matrix are decomposed, and the effective eigenvector is selected to be sparse representation in the redundant dictionary, and the low-dimensional sparse linear model is constructed. Finally, the angle information of the target is obtained by the reconstruction algorithm, which reduces the computational complexity of the original received signal reconstruction by sparse reconstruction of the eigenvector. And it still has good estimation performance under low SNR and low shot. It combines the algorithm based on high order cumulant with the theory of compression perception. In this paper, a method of MIMO radar transceiver angle estimation based on high order cumulant and sparse representation is proposed. Firstly, the fourth order cumulant is not sensitive to Gao Si noise. Then the fourth order cumulant matrix is decomposed to obtain the signal subspace. The eigenvector is represented sparsely in the appropriate redundant dictionary and the sparse coefficient is solved by the reconstruction algorithm. The simulation results show that the proposed method not only can effectively suppress Gao Si color noise, but also has high robustness.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51
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本文编号：1614240