基于稀疏重构的L型阵列MIMO雷达降维DOA估计

发布时间：2018-10-04 18:48

【摘要】：针对L型阵列多输入多输出(multiple-input multiple-output,MIMO)雷达二维空间角估计问题,提出一种基于协方差矩阵联合稀疏重构的降维波达方向(direction of arrival,DOA)估计算法。该算法根据L型阵列MIMO雷达联合流型矢量的特点,通过降维矩阵的设计及回波数据的降维变换,最大程度地去除了所有的冗余数据;通过协方差矩阵联合构造稀疏线性模型,将2维角参量空间映射到1维空间,极大降低字典长度和求解复杂度的同时,不牺牲阵列孔径,实现了二维空间角度的有效估计和参数的自动配对。理论分析与实验仿真表明:与RD_MUSIC算法相比,本文降维处理有效提高阵元利用率的同时,最大程度地降低了回波数据的维数;与传统子空间类算法相比,基于协方差矩阵联合构造的稀疏线性模型充分利用了阵列孔径,无需预先估计目标数目,参数估计性能在低信噪比及小快拍数据长度下优势明显。最后,仿真结果验证了本文理论分析的正确性和算法的有效性。
[Abstract]:To solve the two-dimensional spatial angle estimation problem of L-type array multiple input multiple output (multiple-input multiple-output,MIMO) radar, a reduced dimension directional of arrival (direction of arrival,DOA) estimation algorithm based on covariance matrix combined with sparse reconstruction is proposed. According to the characteristics of the combined flow pattern vector of L-type array MIMO radar, the algorithm removes all redundant data to the maximum extent through the design of dimensionality reduction matrix and the dimensionality reduction transformation of echo data, and constructs a sparse linear model by covariance matrix. The 2-D angular parameter space is mapped to 1-dimensional space, which greatly reduces the dictionary length and the complexity of the solution without sacrificing the array aperture, and realizes the effective estimation of the two-dimensional spatial angle and the automatic matching of the parameters. Theoretical analysis and experimental simulation show that compared with RD_MUSIC algorithm, the dimensionality reduction algorithm in this paper can effectively improve the utilization ratio of array elements and reduce the dimension of echo data to the maximum extent. The sparse linear model based on covariance matrix makes full use of array aperture and does not need to estimate the number of targets in advance. The performance of parameter estimation is superior to that of low SNR and small rapid-shot data length. Finally, the simulation results verify the correctness of the theoretical analysis and the validity of the algorithm.
【作者单位】：电子工程学院通信对抗系;
【基金】：国家自然科学基金(60702015)资助课题
【分类号】：TN957.51

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