基于SVD和分数阶低阶统计量的相干源DOA估计

Sen Li, Zhenxing Liang
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引用次数: 2

摘要

本文主要研究了以α-稳定分布为模型的脉冲噪声场中相干源的到达方向估计问题。提出了基于奇异值分解(SVD)和分数阶低阶统计量的两种新算法。该方法利用接收数据的分数阶低阶统计量矩阵中最大特征值对应的特征向量重建矩阵,然后得到重建矩阵的自协方差矩阵。噪声子空间可通过矩阵的奇异值分解(SVD)得到,该矩阵由得到的自协方差及其复共轭逆进行重构。仿真结果验证了该方法在脉冲噪声中相干源DOA估计的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DOA estimation of the coherent sources based on SVD and the fractional lower order statistics
This paper is mainly to deal with the problem of direction of arrival (DOA) estimations of coherent sources in impulsive noise fields modeled as α-stable distribution. Two new algorithms based on singular value decomposition (SVD) and fractional lower order statistics are proposed. The proposed methods rebuild a matrix by using the eigenvector corresponding to the largest eigenvalue obtained from the fractional lower order statistics matrix of the received data, then the auto-covariance matrix of the rebuilt matrix can be obtained. The noise subspace can be gained by SVD of the matrix which is reconstructed by the equal of the obtained auto-covariance and its complex-conjugation inversion. Simulation results demonstrate the effectiveness of the proposed methods for estimation DOA of the coherent sources in impulsive noise.
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