[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 基于主特征矢量的相干信号DOA估计算法-A Modified DOA Estimation Algorithm of Coherent Signals Based on Main Feature Vectors
文章摘要
唐晓杰,何明浩,韩俊,李铭伟.基于主特征矢量的相干信号DOA估计算法[J].空军工程大学学报:自然科学版,2019,20(6):54-60
基于主特征矢量的相干信号DOA估计算法
A Modified DOA Estimation Algorithm of Coherent Signals Based on Main Feature Vectors
  
DOI:
中文关键词: DOA估计  相干信号  增广主特征矢量  加权最小二乘法
英文关键词: DOA estimation  coherent signals  augmented main feature eigenvectors  weighted least squares
基金项目:湖北省自然科学基金(2016CFB288)
作者单位
唐晓杰,何明浩,韩俊,李铭伟 空军预警学院信息对抗系武汉430019 
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中文摘要:
      为了进一步提高相干信号DOA估计的精度,提出了一种基于主特征矢量的相干信号DOA估计改进算法。算法选取了信号子空间对应的主特征向量,将其进行反向共轭变换得到增广主特征矢量,再构造线性预测方程,利用加权最小二乘法求解预测方程中的多项式系数,最后求根得到信号的DOA估计。改进算法克服了PUMA算法在信号完全相干条件下性能恶化的缺陷,当信号完全相干以及部分相干时都具有良好的性能,并且提高了最大可分辨信号数,当信噪比较低、快拍数较少时仍保持较高精度。相比于PUMA算法,当信号部分相干时,文中算法的均方根误差约降低了0.2°;当信号完全相干时,均方根误差约降低了0.8°。通过与多种解相干算法进行比较,算法性能得到了验证。
英文摘要:
      In order to further improve the DOA estimation accuracy of coherent signals, a modified algorithm based on main feature vectors is proposed. First, the main eigenvectors corresponding to the signal subspace are selected, and the inverse conjugate transformation is performed to obtain the augmented main feature eigenvectors. Then, the linear prediction equation is constructed, and the weighted least squares method is used to solve the polynomial coefficients in the prediction equation. Finally, the DOA of the signals is obtained by finding the root of the polynomial. The modified algorithm overcomes the weakness of PUMA algorithm under conditions of the deteriorated severely performance and the completely cohered signals, and is good in performance. It’s no matter whether the signals are completely coherent or partially coherent. The maximum number of the resolvable signals are improved. The high precision can be obtained when the signal to noise ratio is low and the number of snapshots is small. Compared with the PUMA algorithm, when the signal is partially coherent, the root mean square error of the proposed algorithm is reduced by about 0.2°; When the signal is completely coherent, the root mean square error is reduced by about 0.8°.The performance of the algorithm is verified by comparison with various decoherence algorithms.
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