基于矩阵补全的二阶统计量重构DOA估计方法

王洪雁; 房云飞; 裴炳南

doi:10.11999/JEIT170826

基于矩阵补全的二阶统计量重构DOA估计方法

doi: 10.11999/JEIT170826

基金项目:

国家自然科学基金(61301258, 61271379)，中国博士后科学基金(2016M590218)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-08-23
- 修回日期: 2018-01-08
- 刊出日期: 2018-06-19

Matrix Completion Based Second Order Statistic Reconstruction DOA Estimation Method

Funds:

The National Natural Science Foundation of China (61301258, 61271379), China Postdoctoral Science Foundation (2016M590218)

摘要

摘要: 该文针对传统波达方向角(DOA)估计算法在非均匀噪声下角度估计精度差及分辨率低的问题，基于矩阵补全理论，提出一种二阶统计量域下加权L1(MC-WLOSRSS)稀疏重构DOA估计算法。首先，基于矩阵补全方法，引入弹性正则化因子将接收信号协方差矩阵重构为无噪声协方差矩阵；而后在二阶统计量域下通过矩阵求和平均将无噪声协方差矩阵多矢量问题转化为单矢量问题；最后利用稀疏重构加权L1范数实现DOA参数估计。数值仿真表明，与传统MUSIC, IL1-SRACV, L1-SVD子空间算法及稀疏重构加权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DOA估计性能，且在低信噪比条件下，亦具有较高估计精度和分辨力。
- 波达方向 /
- 非均匀噪声 /
- 矩阵补全 /
- 二阶统计量 /
- 加权L1范数
Abstract: Focusing on the problem of poor accuracy and low resolution of traditional Direction Of Arrival (DOA) estimation algorithm in the presence of non-uniform noise, based on the Matrix Complement theory, a Weighted L1 Sparse Reconstruction DOA estimation algorithm is developed under the Second-order Statistical domain (MC-WLOSRSS) in this paper. Following the matrix completion approach, the regularization factor is firstly introduced to reconstruct the signal covariance matrix reconstruction as a noise-free covariance matrix. After that, the multi-vector problem of the noise-free covariance matrix can be transformed into a single vector one by exploiting sum-average operation for matrix in the second-order statistical domain. Finally, the DOA can be complemented by employing the sparse reconstruction weighted L1 norm. Numerical simulations show that the proposed algorithm outperforms the traditional DOA algorithms such as MUltiple SIgnal Classification (MUSIC), Improved L1-SRACV (IL1-SRACV), L1-norm-Singular Value Decomposition (L1-SVD) subspace and sparse reconstruction weighted L1 methods in the following respects: suppressing the influence of the non-uniform noise significantly, bettering DOA estimation performance, as well as improving estimation accuracy and resolution with low Signal-Noise Ratio (SNR).
- Direction Of Arrival (DOA) /
- Non-uniform noise /
- Matrix Completion (MC) /
- Second-order statistics /
- Weighted L1 norm

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