De-noised estimation of the weather Doppler spectrum by the wavelet method

Lagha, Mohand; Tikhemirine, Mohamed; Bergheul, S.; Rezoug, Tahar; Bettayeb, Maâmar

doi:10.1016/j.dsp.2012.08.001

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…On the other hand, for a non-stationary non-sinusoidal signal like a noisy transit signal, the wavelet denoising is much more efficient than a frequency-based filtering technique in terms of signal reconstruction and denoised S/N (Barsanti & Gilmore 2011;Lagha et al 2013). Wavelets have already been used extensively in the light curve noise analysis and filtering (Cubillos et al 2017;Waldmann 2014).…”

Section: Treatment Of Noisementioning

confidence: 99%

Precise Photometric Transit Follow-up Observations of Five Close-in Exoplanets: Update on Their Physical Properties

Chakrabarty

Sengupta

2019

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We report the results of the high precision photometric follow-up observations of five transiting hot jupiters -WASP-33b, WASP-50b, WASP-12b, HATS-18b and HAT-P-36b. The observations are made from the 2m Himalayan Chandra Telescope at Indian Astronomical Observatory, Hanle and the 1.3m J. C. Bhattacharyya Telescope at Vainu Bappu Observatory, Kavalur. This exercise is a part of the capability testing of the two telescopes and their back-end instruments. Leveraging the large aperture of both the telescopes used, the images taken during several nights were used to produce the transit light curves with high photometric S/N (> 200) by performing differential photometry. In order to reduce the fluctuations in the transit light curves due to various sources such as stellar activity, varying sky transparency etc. we preprocessed them using wavelet denoising and applied Gaussian process correlated noise modeling technique while modeling the transit light curves. To demonstrate the efficiency of the wavelet denoising process we have also included the results without the denoising process. A state-of-the-art algorithm used for modeling the transit light curves provided the physical parameters of the planets with more precise values than reported earlier.

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Section: Treatment Of Noisementioning

confidence: 99%

Precise Photometric Transit Follow-up Observations of Five Close-in Exoplanets: Update on Their Physical Properties

Chakrabarty

Sengupta

2019

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“…Therefore, researchers have developed several approaches to solve this problem, such as using the filtering technique [52]- [54], thresholding technique [55], [56], and other techniques [57], [58]. WT is one of the powerful techniques for non-stationary signal denoising [44], [59]- [61]. WT has five parameters, with each parameter having different types ( Table 1) the success of EEG signal denoising relies on the selection of WT parameters.…”

Section: A Wavelet Denoising Principle For Non-stationary Signalsmentioning

confidence: 99%

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

et al. 2020

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Background. The most common and successful technique for signal denoising with nonstationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experimentally set. Fortunately, the optimality of the combination of these parameters can be measured in advance by using the mean squared error (MSE) function. Method. In this paper, five powerful metaheuristic algorithms are proposed to find the optimal WT parameters for EEG signal denoising which are harmony search (HS), β-hill climbing (β-hc), particle swarm optimization (PSO), genetic algorithm (GA), and flower pollination algorithm (FPA). It is worth mentioning that this is the initial investigation of using optimization methods for WT parameter configuration. This paper then examines which efficient algorithm has obtained the minimum MSE and the best WT parameter configurations. Result. The performance of the proposed algorithms is tested using two standard EEG datasets, namely, Kiern's EEG dataset and EEG Motor Movement/Imagery dataset. The results of the proposed algorithms are evaluated using five common criteria: signal-to-noise-ratio (SNR), SNR improvement, mean square error (MSE), root mean square error (RMSE), and percentage root mean square difference (PRD). Interestingly, for almost all evaluating criteria, FPA achieves the best parameters configuration for WT and empowers this technique to efficiently denoise the EEG signals for almost all used datasets. To further validate the FPA results, a comparative study between the FPA results and the results of two previous studies is conducted, and the findings favor to FPA. Conclusion. In conclusion, the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy. Therefore, using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method.

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“…The principal components are considered as signal subspaces and the rest are noise subspaces. The purpose of noise reduction is achieved by retaining the signal subspace only [11]; Benchebha et al [12,13].…”

Section: Ppp Based On Pca De-noisingmentioning

confidence: 99%

Atmospheric turbulence detection by PCA approach

Zhang

Xiao

Lan

et al. 2018

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By detecting the severe meteorological situations on flight route, airborne weather radar (WXR) can ensure the safety of the aircraft and on-board personnel. Among these critical weather conditions, atmospheric turbulence is one of the main factors that affect flight safety. Atmospheric turbulence detection method that the current WXR adopts mainly is the pulse pair processing (PPP) method, which estimates Doppler spectrum width of weather target echo and compares it with a threshold to determine whether this weather target is turbulence or not. PPP method is simple and easy to implement, but the performance of this method under the condition of low signal-to-noise ratio (SNR) is poor. In this paper, we propose a new turbulence detection method based on the principal component analysis (PCA) approach. This new method uses PCA approach to preprocess the weather target echo and divides it into two parts: the principal component part as signal and the rest part as noise, so as to realize the de-noising function of PCA approach, and it is then combined with PPP method to estimate the spectrum width. Due to the good de-noising performance of PCA approach, this new method improves the detection performance of traditional PPP method especially under the condition of low SNR.

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De-noised estimation of the weather Doppler spectrum by the wavelet method

Cited by 12 publications

References 11 publications

Precise Photometric Transit Follow-up Observations of Five Close-in Exoplanets: Update on Their Physical Properties

Precise Photometric Transit Follow-up Observations of Five Close-in Exoplanets: Update on Their Physical Properties

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

Atmospheric turbulence detection by PCA approach

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