An Adaptive SVD Method for Solving the Pass-Region Problem in S-Transform Time-Frequency Filters
An Adaptive SVD Method for Solving the Pass-Region Problem in S-Transform Time-Frequency Filters
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DOI：
10.1049/cje.2015.01.019
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发表时间：
2015
影响因子：
1.2
通讯作者：
Baiqiang Yin;Yigang He;Bing Li;Lei Zuo;Lifen Yuan
中科院分区：
文献类型：
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作者：
Baiqiang Yin;Yigang He;Bing Li;Lei Zuo;Lifen Yuan
S变换(ST)是一种很好的时频滤波工具。影响滤波性能的因素有两个：S逆变换算法和时频域的通域。推导了一种新的矩阵IST算法，并提出了求解通域问题的自适应奇异值分解(SVD)方法。前者可避免时频滤波中的重构误差，后者能有效区分信号与噪声的通域。滤波可以通过去除较小的奇异值而保留较大的奇异值来实现。在ST时频域建立了加性噪声扰动模型，分析了基于矩阵IST的噪声扰动模型的有效阶。仿真结果表明，该方法在低信噪比下比现有的方法具有更高的检测精度，且不需要计算噪声统计特性。算例验证了该方法的有效性。
S-transform (ST) is an excellent tool for time-frequency filter. There are two factors that influence filtering performance: Inverse s-transform (IST) algorithms and the pass-regions in time-frequency domain. A novel matrix IST algorithm is derived and an adaptive Singular value decomposition (SVD) method for solving the pass-region problem is proposed. The former can avoid reconstructing errors in time-frequency filtering; the latter is effective to distinguish the pass-region of signal from noise. Filter can be realized by removing the smaller singular values and keeping the larger singular values. An additive noise perturbation model is built in ST time-frequency domain and the effective rank of noise perturbation model based on matrix IST is analyzed. Simulation results indicate that the proposed SVD method can provide higher precision than the existing ones at low signal-to-noise ratio and does not need to compute the noise statistics property. Illustrative examples verify the effectiveness of proposed method.