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Simple and Efficient Algorithm for Improving the MDL Estimator of the Number of Sources

机译:一种简单有效的算法,用于改进源数量的MDL估计器

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We propose a simple algorithm for improving the MDL (minimum description length) estimator of the number of sources of signals impinging on multiple sensors. The algorithm is based on the norms of vectors whose elements are the normalized and nonlinearly scaled eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. Such norms are used to discriminate the largest eigenvalues from the remaining ones, thus allowing for the estimation of the number of sources. The MDL estimate is used as the input data of the algorithm. Numerical results unveil that the so-called norm-based improved MDL (iMDL) algorithm can achieve performances that are better than those achieved by the MDL estimator alone. Comparisons are also made with the well-known AIC (Akaike information criterion) estimator and with a recently-proposed estimator based on the random matrix theory (RMT). It is shown that our algorithm can also outperform the AIC and the RMT-based estimator in some situations.
机译:我们提出了一种简单的算法,用于改进撞击在多个传感器上的信号源数量的MDL(最小描述长度)估计器。该算法基于矢量范数,其元素是接收信号协方差矩阵的归一化和非线性缩放特征值以及相应的归一化索引。这样的规范用于将最大特征值与其余特征值区分开,从而可以估算光源的数量。 MDL估计用作算法的输入数据。数值结果表明,所谓的基于规范的改进的MDL(iMDL)算法可以实现比仅由MDL估计器获得的性能更好的性能。还与著名的AIC(Akaike信息标准)估计器以及最近基于随机矩阵理论(RMT)提出的估计器进行了比较。结果表明,在某些情况下,我们的算法还可以胜过基于AIC和基于RMT的估计器。

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