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Reconstruction of eigenvalues in noise subspace for uneuqual power sources DOA estimation

机译:不等功率源DOA估计的噪声子空间特征值重构

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Conventional subspace-based methods for solving the narrow-band multiple sources location problems show high resolution, provided that the power of each source is identical. However this assumption may be not suitable for targets in electronic warfare. To deal with sources with unequal power, in this paper, we propose a method that reconstructs the eigenvalues of the noise subspace in covariance matrix to improve the resolution ability of the subspace-based method. The reconstruction of the eigenvalues in noise subspace has changed the proportion of the signal to noise in covariance matrix. Then, the signal ingredient existing in the noise subspace which is caused by finite snapshots is reduced by applying another eigenvalue decomposition (EVD) in the reconstructed covariance matrix. Simulation results indicate that our method has a better performance than MUSIC and subspace method developed by Ali Olfat et al, which prove the effectiveness of our method.
机译:如果每个源的功率相同,则用于解决窄带多源定位问题的基于子空间的常规方法将显示高分辨率。但是,该假设可能不适用于电子战中的目标。为了处理功率不相等的源,本文提出了一种在协方差矩阵中重构噪声子空间特征值的方法,以提高基于子空间的方法的分辨能力。噪声子空间中特征值的重构改变了协方差矩阵中信号与噪声的比例。然后,通过在重建的协方差矩阵中应用另一个特征值分解(EVD),减少了由有限快照导致的存在于噪声子空间中的信号成分。仿真结果表明,该方法具有比Ali Olfat等人开发的MUSIC和子空间方法更好的性能,证明了该方法的有效性。

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