为提高非均匀噪声下波达方向(direction of arrival,DOA)角估计算法的估计精度和分辨率,基于低秩矩阵恢复理论,提出了一种二阶统计量域下的加权l1稀疏重构DOA估计算法。该算法基于低秩矩阵恢复方法,引入弹性正则化因子将接收信号协方差...为提高非均匀噪声下波达方向(direction of arrival,DOA)角估计算法的估计精度和分辨率,基于低秩矩阵恢复理论,提出了一种二阶统计量域下的加权l1稀疏重构DOA估计算法。该算法基于低秩矩阵恢复方法,引入弹性正则化因子将接收信号协方差矩阵重构问题转换为可获得高效求解的半定规划(semidefinite programming,SDP)问题以重构无噪声协方差矩阵;而后在二阶统计量域下利用稀疏重构加权l1范数实现DOA参数估计。数值仿真表明,与传统MUSIC、l1-SVD及加权l1算法相比,所提算法能显著抑制非均匀噪声影响,具有较好的DOA参数估计性能,且在低信噪比条件下,所提算法具有较高的角度分辨力和估计精度。展开更多
现有的波达方向(Direction Of Arrival,DOA)和极化参数估计方法大多基于子空间理论.本文从稀疏信号重构角度出发,提出了一种新的DOA和极化角度估计算法.该算法首先构建一个只包含DOA信息的累积量矩阵模型,然后基于加权l1范数最小化获得...现有的波达方向(Direction Of Arrival,DOA)和极化参数估计方法大多基于子空间理论.本文从稀疏信号重构角度出发,提出了一种新的DOA和极化角度估计算法.该算法首先构建一个只包含DOA信息的累积量矩阵模型,然后基于加权l1范数最小化获得DOA估计.在DOA估计的基础上,进一步通过求和平均运算构建三个包含不同极化信息的累积量向量模型,利用Zhang惩罚进行稀疏性约束,获得近似无偏的极化角度估计.阐述了如何利用极化信息来区分两个入射角度一样的信源信号.计算机仿真结果验证了所提算法的有效性.展开更多
Based on the range space property （RSP）, the equivalent conditions between nonnegative solutions to the partial sparse and the corresponding weighted l1-norm minimization problem are studied in this paper. Different...Based on the range space property （RSP）, the equivalent conditions between nonnegative solutions to the partial sparse and the corresponding weighted l1-norm minimization problem are studied in this paper. Different from other conditions based on the spark property, the mutual coherence, the null space property （NSP） and the restricted isometry property （RIP）, the RSP- based conditions are easier to be verified. Moreover, the proposed conditions guarantee not only the strong equivalence, but also the equivalence between the two problems. First, according to the foundation of the strict complemenrarity theorem of linear programming, a sufficient and necessary condition, satisfying the RSP of the sensing matrix and the full column rank property of the corresponding sub-matrix, is presented for the unique nonnegative solution to the weighted l1-norm minimization problem. Then, based on this condition, the equivalence conditions between the two problems are proposed. Finally, this paper shows that the matrix with the RSP of order k can guarantee the strong equivalence of the two problems.展开更多
文摘为提高非均匀噪声下波达方向(direction of arrival,DOA)角估计算法的估计精度和分辨率,基于低秩矩阵恢复理论,提出了一种二阶统计量域下的加权l1稀疏重构DOA估计算法。该算法基于低秩矩阵恢复方法,引入弹性正则化因子将接收信号协方差矩阵重构问题转换为可获得高效求解的半定规划(semidefinite programming,SDP)问题以重构无噪声协方差矩阵;而后在二阶统计量域下利用稀疏重构加权l1范数实现DOA参数估计。数值仿真表明,与传统MUSIC、l1-SVD及加权l1算法相比,所提算法能显著抑制非均匀噪声影响,具有较好的DOA参数估计性能,且在低信噪比条件下,所提算法具有较高的角度分辨力和估计精度。
文摘现有的波达方向(Direction Of Arrival,DOA)和极化参数估计方法大多基于子空间理论.本文从稀疏信号重构角度出发,提出了一种新的DOA和极化角度估计算法.该算法首先构建一个只包含DOA信息的累积量矩阵模型,然后基于加权l1范数最小化获得DOA估计.在DOA估计的基础上,进一步通过求和平均运算构建三个包含不同极化信息的累积量向量模型,利用Zhang惩罚进行稀疏性约束,获得近似无偏的极化角度估计.阐述了如何利用极化信息来区分两个入射角度一样的信源信号.计算机仿真结果验证了所提算法的有效性.
基金Research supported by the National Natural Science Foundation of China under Grant 61672005
文摘Based on the range space property （RSP）, the equivalent conditions between nonnegative solutions to the partial sparse and the corresponding weighted l1-norm minimization problem are studied in this paper. Different from other conditions based on the spark property, the mutual coherence, the null space property （NSP） and the restricted isometry property （RIP）, the RSP- based conditions are easier to be verified. Moreover, the proposed conditions guarantee not only the strong equivalence, but also the equivalence between the two problems. First, according to the foundation of the strict complemenrarity theorem of linear programming, a sufficient and necessary condition, satisfying the RSP of the sensing matrix and the full column rank property of the corresponding sub-matrix, is presented for the unique nonnegative solution to the weighted l1-norm minimization problem. Then, based on this condition, the equivalence conditions between the two problems are proposed. Finally, this paper shows that the matrix with the RSP of order k can guarantee the strong equivalence of the two problems.
