基于矩陣補(bǔ)全的二階統(tǒng)計(jì)量重構(gòu)DOA估計(jì)方法
doi: 10.11999/JEIT170826
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61301258, 61271379)，中國(guó)博士后科學(xué)基金(2016M590218)
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)
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摘要: 該文針對(duì)傳統(tǒng)波達(dá)方向角(DOA)估計(jì)算法在非均勻噪聲下角度估計(jì)精度差及分辨率低的問題，基于矩陣補(bǔ)全理論，提出一種二階統(tǒng)計(jì)量域下加權(quán)L1(MC-WLOSRSS)稀疏重構(gòu)DOA估計(jì)算法。首先，基于矩陣補(bǔ)全方法，引入彈性正則化因子將接收信號(hào)協(xié)方差矩陣重構(gòu)為無噪聲協(xié)方差矩陣；而后在二階統(tǒng)計(jì)量域下通過矩陣求和平均將無噪聲協(xié)方差矩陣多矢量問題轉(zhuǎn)化為單矢量問題；最后利用稀疏重構(gòu)加權(quán)L1范數(shù)實(shí)現(xiàn)DOA參數(shù)估計(jì)。數(shù)值仿真表明，與傳統(tǒng)MUSIC, IL1-SRACV, L1-SVD子空間算法及稀疏重構(gòu)加權(quán)L1算法相比，所提算法能顯著抑制非均勻噪聲影響，具有較好DOA估計(jì)性能，且在低信噪比條件下，亦具有較高估計(jì)精度和分辨力。
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關(guān)鍵詞:
- 波達(dá)方向 /
- 非均勻噪聲 /
- 矩陣補(bǔ)全 /
- 二階統(tǒng)計(jì)量 /
- 加權(quán)L1范數(shù)
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). -
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