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Accurate DOA Estimation Based on Real-Valued Singular Value Decomposition

机译:基于实值奇异值分解的精确DOA估计

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In this paper, an accurate direction-of-arrival (DOA) estimator is developed based on the real-valued singular value decomposition (SVD) of covariance matrix. Unitary transform on the complex-valued covariance matrix is first applied, and then SVD performs on the resulting real-valued data matrix. The singular vector is then utilized with a weighted least squares (WLS) method to achieve DOA estimation. The performance of the proposed algorithm is compared with several state-of-the-art methods as well as the CRB. The results indicate the accuracy and effectiveness of the proposed method.
机译:本文基于协方差矩阵的实值奇异值分解(SVD),开发了一种精确的到达方向(DOA)估计器。首先对复数值协方差矩阵进行变换,然后SVD对所得的实数值数据矩阵执行运算。然后,将奇异矢量与加权最小二乘(WLS)方法结合使用以实现DOA估计。将所提出算法的性能与几种最新方法以及CRB进行了比较。结果表明了该方法的准确性和有效性。

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