A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

Yang, Xu; Xing, Hongyan; Liu, Zhuang

doi:10.1109/access.2021.3051479

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…In 2021, Yang emphasized the importance of denoising for the study of AEFs and proposed a complementary ensemble empirical mode decomposition with adaptive noise and a Savitzky-Golay filter (CEEMDAN-SG) for AEF noise reduction [14]. However, CEEMDAN, being an iterative signal decomposition method, demands a considerable amount of time to process intricate signals [15], rendering it unsuitable for real-time warning requirements.…”

Section: Eewt-asgmentioning

confidence: 99%

“…The more intricate the frequency components of the AEF signal, the greater the number of decomposed modes. In order to ascertain whether each component contained noise, we employed an autocorrelation analysis, inspired by Yang, to make this determination [14]. We computed the normalized autocorrelation function of D i (n) as follows:…”

Section: Eewt-asgmentioning

confidence: 99%

“…If the data window is excessively wide, it may result in the loss of valuable information in the signal. Conversely, if the data window is too narrow to effectively filter the signal, opting for a large polynomial degree might introduce new noise, while selecting a small polynomial degree could cause distortion due to oversmoothing of the signal [14,21]. Given that the AEF signal is non-stationary, fixing both the data window size and the polynomial degree in signal smoothing could lead to the loss of original information in the signal.…”

Section: Eewt-asgmentioning

confidence: 99%

“…A method for using energy for lightning risk warnings was also proposed [13]. However, previous studies have failed to account for the impact of noise on the AEF [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Li,

Yang,

Yin

et al. 2023

Atmosphere

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The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on canceling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky–Golay filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning events. This method can issue a warning signal up to 22 min in advance for lightning processes.

show abstract

Section: Eewt-asgmentioning

confidence: 99%

Section: Eewt-asgmentioning

confidence: 99%

Section: Eewt-asgmentioning

confidence: 99%

“…A method for using energy for lightning risk warnings was also proposed [13]. However, previous studies have failed to account for the impact of noise on the AEF [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Li,

Yang,

Yin

et al. 2023

Atmosphere

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show abstract

“…The target of the outdoor SSL is the thunder source point, and the experimental site is shown in figure 11(a). Before the experiment, by using the complementary ensemble empirical mode decomposition with adaptive noise and Savitzky-Golay filtering (CEEMDAN-SG), the sound source signal collected by the microphone array group is denoised [24]. More specifically, after CEEMDAN is used to decompose the sound source signal into a series of intrinsic mode function components, the signal is reconstructed after SG filtering of those noise-dominant components.…”

Section: Ssl Outdoor Experimentsmentioning

confidence: 99%

A data complementary method for sound source localization based on four-element microphone array groups

Yang¹,

Xing²,

Yan³

2021

Meas. Sci. Technol.

Self Cite

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Data loss or distortion has adverse effects on sound source localization (SSL) accuracy and stability. Based on the sound source azimuth estimation algorithm, this paper establishes a model of a single four-element main microphone array, and proposes a data complementary method of SSL. The calculation formula of sound source coordinates is derived from the main array. Then, the effectiveness of the proposed algorithm is verified by the study of array parameters. Besides the sound velocity and elevation angle, the array parameters also include the distance between two array elements. Array groups are established surrounding the main array on the horizontal plane, through which the localization data are collected and complementarily processed, so as to relocate the sound source. The results show that the coordinate error rate against the theoretical data is approximately 5% and the error rate of the angle is approximately 0.5%. The SSL efficiency is well guaranteed.

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

Yang,

Xing,

et al. 2023

Digital Communications and Networks

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A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

Cited by 12 publications

References 20 publications

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

A data complementary method for sound source localization based on four-element microphone array groups

An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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