Empirical Mode Decomposition Algorithm Research &amp; Application of Mie Lidar Atmospheric Backscattering Signal

Fatai, 郑发泰 Zheng; Dengxin, 华灯鑫 Hua; Awei, 周阿维 Zhou

doi:10.3788/cjl20093605.1068

Cited by 13 publications

(4 citation statements)

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“…In order to further evaluate the pros and cons of the noise cancellation effect, this paper introduces a signal-to-noise ratio (SNR) and mean square error (MSE) to evaluate the de-noising EMG signal . The two indicators [10], respectively, is expressed as:…”

Section: Experimental Design and Analysismentioning

confidence: 99%

EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method

Chen

Wang

Liu

2013

AMM

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Surface Electromyographic (SEMG) signal is characterized by physiological noise contained,in order to eliminate inclusion noise. In wavelet packet analysis domain,after analysising the traditional soft-threshold and hard-threshold de-noising method of characteriscs,a new method which is μ rhythm threshold method is provided to improve threshold function. In Matlab7.0,the relevant procedures and simulation show that the method can not only solve the hard-threshold de-noising continuous problem,but also solve the detects before and after the threshold treatment of the soft-threshold wavelet coefficients constant deviation. Meanwhile,the application of the method in the wavelet analysis shows that wavelet packet μ rhythm threshold method de-noising significantly better than wavelet μ rhythm threshold method,and proves that the wavelet packet has remarkable capacity to de-noising.

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Section: Experimental Design and Analysismentioning

confidence: 99%

EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method

Chen

Wang

Liu

2013

AMM

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“…In 2009, Zheng et al used the EMD method to denoise lidar return signals, and the effect was particularly significant in their case. 4 In the same year, Kopsinis et al combined the wavelet threshold method with the EMD denoising method, which provided a certain improvement in its denoising effect on the return signal. 5 In 2011, Zhang et al combined EEMD with Savitzky-Golay filtering technology, where the high-frequency noisy intrinsic mode function (IMF) component was denoised and the remaining IMF component was then reconstructed to obtain the filtered lidar return signal.…”

Section: Introductionmentioning

confidence: 99%

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

Zhao,

Mao

2024

Journal of Applied Physics

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Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

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“…In 1998, Huang et al developed a Hilbert-Huang transform based on Hilbert analysis, which divided the feature components of the signal into multiple levels and analyzed the feature components on these levels [7]. In 2009, Zheng et al employed the EMD method to suppress lidar noise and obtained promising results [8]. In 2014, Konstantin et al proposed the variational mode decomposition (VMD) algorithm [9], which attracted the attention of many researchers due to its superiority in dealing with nonlinear and non-stationary signals.…”

Section: Introductionmentioning

confidence: 99%

A novel lidar signal noise reduction algorithm based on improved deep belief network

Li,

Mao,

Gong

et al. 2023

Phys. Scr.

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To reduce noise in the lidar return signal, an improved deep belief network (DBN) denoising algorithm is proposed in this study. In the traditional implementation process of DBN, a multi-layer fully connected network is realized by stacking restricted Boltzmann machines (RBMs). However, the RBM is an undirected graph model, and there is no clear causal relationship between random variable nodes. The denoising autoencoder (DAE) can avoid this problem and produce field generalization performance by adding random contamination during training and stacking, thereby achieving better performance than the traditional DBN. In this study, a new multi-layer DBN called DADBN is implemented by stacking DAE and RBM. First, the multi-layer DAE is placed in the beginning layer of the network as the primary filter of the signal to provide data dimensionality reduction and feature extraction. Then, the RBM is used as the lower layer, the hidden layer is calculated according to the initial value of the visible layer by the contrast of the divergence algorithm, and the visible layer is reconstructed from the samples of the hidden layer. And the sparse representation penalty is added to the RBM model to solve the assimilation phenomenon of hidden layer nodes in the RBM model. It not only optimizes the log-likelihood function when training data, but also makes the probability of each hidden layer node being activated tend to a minimum value, so as to sparsely activate the sparse hidden layer nodes. The weight matrix W is obtained by computing the results of the hidden layer twice. Finally, the original input signal was reconstructed by Gibbs sampling layer and the reconstructed signal was decoded by the decoder to achieve the purpose of noise reduction. To verify its effectiveness, this method is compared with four other denoising methods: wavelet packet algorithm, complete ensemble empirical modal decomposition (CEEMDAN), wavelet transform and empirical mode decomposition (WT-EMD), and wavelet transform-variational mode decomposition (WT-VMD). The noise reduction effect of DADBN is better than that of the other four methods, thus effectively eliminating complex noise in the lidar detection signal.

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Empirical Mode Decomposition Algorithm Research & Application of Mie Lidar Atmospheric Backscattering Signal

Cited by 13 publications

References 0 publications

EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method

EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

A novel lidar signal noise reduction algorithm based on improved deep belief network

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