基于互相關(guān)抽樣分解的分布式非圓信號DOA快速估計

崔維嘉; 代正亮; 巴斌; 魯航

doi:10.11999/JEIT170663

基于互相關(guān)抽樣分解的分布式非圓信號DOA快速估計

doi: 10.11999/JEIT170663

基金項目:

國家自然科學(xué)基金(61401513)

計量
- 文章訪問數(shù): 1148
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-07-06
- 修回日期: 2017-12-11
- 刊出日期: 2018-05-19

Fast DOA Estimation of Distributed Noncircular Sources by Cross-correlation Sampling Decomposition

Funds:

The National Natural Science Foundation of China (61401513)

摘要

摘要: 在非相干分布式非圓信號波達方向(DOA)估計中，針對利用信號非圓特性后輸出矩陣維數(shù)擴展帶來的較大運算量問題，該文提出一種基于互相關(guān)抽樣分解的DOA快速估計算法。該算法僅需要從子陣間的擴展互相關(guān)矩陣中抽樣出少量行元素和列元素，構(gòu)成兩個低維子矩陣，進而通過低秩近似分解便可快速地同時求出左右奇異矢量，即分別對應(yīng)兩個子陣的信號子空間，避免了計算整個互相關(guān)矩陣及其奇異值分解運算；最后利用兩個子陣信號子空間的旋轉(zhuǎn)不變性通過最小二乘得到DOA估計。仿真分析表明，當行列抽樣數(shù)大于信源數(shù)的兩倍時，所提算法與直接基于互相關(guān)矩陣奇異值分解的非相干分布式非圓信號DOA估計算法性能相近，但復(fù)雜度得到了大幅度降低；而相比于傳統(tǒng)的低復(fù)雜度非相干分布源DOA估計算法，所提算法利用信號非圓特性具有更高的估計性能。
- 陣列信號處理 /
- 非相干分布式非圓信號 /
- 互相關(guān)矩陣 /
- 抽樣
Abstract: In the Direction Of Arrival (DOA) estimation of incoherently distributed noncircular sources, the increase of dimension caused by array output matrix extension can cause a large computational complexity. To solve this problem, a rapid DOA estimation algorithm is proposed based on cross-correlation sampling decomposition. It only needs to calculate two low-dimensional sub-matrices, which are formed by a small number of rows and columns in the extended Cross-Correlation (CC) matrix. On the premise of the sub-matrices, the right and left singular vectors corresponding to two signal subspaces can be simultaneously obtained by the low-rank approximation decomposition, which avoids the calculation of the whole covariance matrix and its singular value decomposition. Finally, the DOA estimation can be obtained by the least squares with the rotation invariance of the signal subspaces. The simulation results show that when the number of samples in the low-dimensional sub-matrix is larger than twice the number of sources, the performance of the proposed algorithm is comparable with the DOA estimation algorithm of incoherently distributed noncircular sources based on the singular value decomposition applying to the CC matrix. Moreover, the proposed algorithm utilizes the noncircular characteristic of the signal to achieve higher estimation performance compared with the traditional low-complexity DOA estimation algorithms of the incoherently distributed sources.
- Array signal processing /
- Incoherently distributed noncircular source /
- Cross-Correlation (CC) matrix /
- Sampling

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