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Comparison of Kalman Filter and Wavelet Filter for Denoising

机译:卡尔曼滤波器和小波滤波器去噪的比较

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This paper presents denoising the signal using Wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0,001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative Wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of Wavelet filter and Kalman filter are shown. Response of Wavelet filter is better when compared with Kalman filter result.
机译:本文介绍了使用小波滤波器和卡尔曼滤波器的信号去噪。噪声为零均值,方差值为0,001。 Kalman滤波器通过使用初始化和传播错误协方差统计来消除信号的干扰或故障。如振荡器系统所示,在大规模模型中实现了卡尔曼滤波器的实施。作为替代小波滤波器已用于同一系统。已经使用了作为正交小波的Coiflet 2。软阈值已应用。分解在级别9中执行。示出了小波滤波器和卡尔曼滤波器的结果。与卡尔曼滤波器结果相比,小波滤波器的响应更好。

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