Sensor network sensing information denoising processing method based on principal component analysis (PCA) and empirical mode decomposition (EMD)

Abstract

The invention discloses a sensor network sensing information denoising processing method based on principal component analysis (PCA) and empirical mode decomposition (EMD). The method comprises the following steps of: performing EMD on a sensing signal to obtain K intrinsic mode function (IMF) components imfk (t) for characterizing time scale and remainder terms; extracting detailed signal information in imf1, calculating energy W[1] of the noise contained in the imf1, and calculating energy of other layers according to W[1]; and performing PCA decomposition on imfk (t), selecting the previous H principal components in a proper number for reconstructing and denoising according to a ratio of the energy of the noise contained in the imfk(t) to obtain the denoised detailed signal information; and accumulating all the denoised detailed signal information and remainder terms to obtain the denoised signals. The method is simple and good in denoising effect.

Description

technical field [0001] The invention relates to a processing method for removing noise, in particular to a PCA and EMD-based sensor network sensing information denoising processing method. Background technique [0002] The wireless sensor network can monitor, sense and process the information of the monitoring area cooperatively in real time, and transmit the information to the user. Due to the influence of the monitoring environment, the perceived information will contain a lot of noise. If these noises are not eliminated, it will lead to the inability to accurately analyze the monitoring information and seriously affect the correctness of subsequent processing. Therefore, how to effectively denoise the sensory information polluted by noise to obtain more accurate sensor measurements is an urgent problem to be solved. [0003] Wavelet analysis has good time-frequency analysis characteristics, and has been widely used in signal denoising. However, when applying wavelet tran...

AI Technical Summary

Problems solved by technology

However, in the threshold denoising method based on modal units, there are still two problems that are difficult to solve: First, the determination of the threshold of modal units is a difficult problem. In existing algorithms, wavelet thresholds are often used or thresholds are selected based on experience, and there is no perfect threshold selection. The second is that the modal units whose extreme values ​​are smaller than the threshold are directly removed in the algorithm, while the modal units whose extreme values ​​are greater than the threshold are directly retained

However, the noise is distributed in the entire IMF, so directly removing the small threshold modal unit will cause part of the signal information to be lost; while directly retaining all points in the large threshold modal unit without processing will cause the noise to be unable to be detected complete removal

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