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Sea clutter hybrid denoising algorithm based on VMD

机译：基于VMD的海杂波混合去噪算法

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摘要

In order to improve the detection accuracy of sea clutter wavelet prediction model further, a sea clutter hybrid denosing algorithm based on variational modal decomposition (VMD) is proposed. The VMD is adopted to decompose the sea clutter signal into a finite number of intrinsic modal functions (IMF) with limited bandwidths of different center frequencies. Then, we analyze the auto-correlation property of the decomposed signal and perform wavelet hard threshold filtering on the modal component with noise characteristics. Reconstructing the filtered component and the residual component to obtain a denosied signal. The sea clutter prediction model based on LSSVM is adopted to verify the denosing result, and the denosing result is evaluated by comparing the predicted root mean square error (RMSE) before and after denosing. Comparing the prediction results of the two groups of experiments, it is not difficult to find that the predicted RMSE after denosing is 0.00055, which is two orders of magnitude lower than the predicted RMS error before denosing (0.0125).
机译：为了进一步提高海杂波小波预测模型的检测精度，提出了一种基于变分模态分解（VMD）的海杂波混合去噪算法。采用VMD将海杂波信号分解为有限数量的本征模态函数（IMF），具有不同中心频率的有限带宽。然后，我们分析了分解信号的自相关特性，并对具有噪声特征的模态分量进行了小波硬阈值滤波。重建滤波后的分量和残差分量以获得去噪信号。采用基于LSSVM的海杂波预测模型验证去噪结果，并通过比较去噪前后的均方根误差（RMSE）评估去噪结果。比较两组实验的预测结果，不难发现去噪后的预测RMSE为0.00055，这比去噪前的预测RMS误差（0.0125）低两个数量级。

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