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基于加權特征投影的信源數估計方法
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西安建筑科技大學(xué)

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國家自然科學(xué)基金(61803294)


Estimation method of the number of sources based on weighted eigenspace projection
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    摘要:

    考慮到色噪聲或低快條件下噪聲特征值發(fā)散,導致基于特征分解的信源數估計方法得到的信號判據值和噪聲判據值區分不明顯,提出了一種基于加權特征投影的信源數估計方法。首先,為了使該方法可適用于低信噪比條件,對陣列接收數據的協(xié)方差矩陣進(jìn)行降噪處理,并利用降噪后協(xié)方差矩陣所有特征值和特征向量構造了一個(gè)用來(lái)區分信號和噪聲的加權空間矩陣;然后,將降噪后的協(xié)方差矩陣在該加權空間矩陣上投影,從而增大了信號判據值與噪聲判據值的差異;最后,結合冪函數的縮放性構建了判決函數,進(jìn)而實(shí)現信源數估計。通過(guò)理論分析和實(shí)驗驗證,該方法不僅適用于白噪聲和色噪聲條件,而且在低快拍和低信噪比條件下優(yōu)勢明顯,尤其是在信源數較多時(shí)效果魯棒。

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    Under the condition of colored noise or low speed, the noise eigenvalues will be diverted, which lead to no significant difference between the signal standard value obtained by the eigen-decomposition-based signal source number estimation method and the noise standard value. Therefore, a method of estimating the number of sources based on weighted eigenspace projection is proposed. First, in order to make the method suitable for low signal-to-noise ratio conditions, the covariance matrix of the data received by the array is denoised, and all eigenvalues and eigenvectors of the denoised covariance matrix are used to construct a noise weighted space matrix. The denoised covariance matrix is projected onto the weighted space matrix to increase the difference between the signal standard value and the noise standard value. Finally, the number of sources is estimated by combining the scalability of the power function to construct decisions. After theoretical analysis and experimental verification, this method is not only suitable for white noise and color noise conditions, but also has obvious advantages under low snapshot and low signal-to-noise ratio conditions, especially when there are more information sources.

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王純,高于晰.基于加權特征投影的信源數估計方法計算機測量與控制[J].,2021,29(9):198-203.

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歷史
  • 收稿日期:2021-02-05
  • 最后修改日期:2021-03-05
  • 錄用日期:2021-03-05
  • 在線(xiàn)發(fā)布日期: 2021-09-23
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