Abstract
In this paper, to realize a better adaptive method for the lightning electric field signal denoising, we firstly compared the decomposition results of three methods called the EMD (empirical mode decomposition), the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), and the EWT (empirical wavelet transform) by artificial signals, respectively, and found that the EWT was better than the other two methods. Then, a MEWT (modified empirical wavelet transform) method based on the EWT was presented for processing the natural lightning signals data. By using our MEWT method, we processed three types of electric field signal data with different frequency bands radiated by the lightning step leader, the cloud pulse and the return stroke, respectively, and the VLF (very low frequency) lightning signals propagating different distances from 500 km to 3500 km, by using the data of the fast electric field change sensors from Nanjing Lightning Location Network (NLLN) in 2018 and the data of the fast electric field change sensors and the VLF electric antennas from the NUIST Wide-range Lightning Location System (NWLLS) in 2021. The results showed that our presented MEWT method could adaptively process different lightning signal data with different frequencies from the step leader, the cloud pulse, and the return stroke; for the lightning VLF signal data from 500 km to 3500 km, the MEWT also achieved a better noise reduction effect. After denoising the signal by using our MEWT, the detection ability of the fast electric field change sensor was improved, and more weak lightning signals could be identified.
Highlights
Lightning is a severe discharge process that occurs in nature and often leads to damage.For an LF/VLF lightning detection system with a working frequency band below 1 MHz, because of the large relative bandwidth, the detected electromagnetic signals are usually complex and mixed with other noise, which infects the accuracy of lightning detecting [1].To achieve a better denoising effect, many researchers have performed a lot of work by using some different methods, which include Fourier, wavelet, EWT as well as EMD and its improvement methods.The easiest way to deal with noise is the filter made by hardware or software, and it is important especially when the information of the noise is known
The results showed that the method achieved a good denoising effect for the LF/VLF electric field signals, further improving the ability of the equipment to detect and identify more pulse signals in the LF/VLF band
To show our method could enhance the fast electric field change sensor detecting ability, we first used the signals received by the VLF electric antennas at each station in the NUIST Wide-range Lightning Location System (NWLLS) network to locate lightning
Summary
The EMD served as an alternative signal processing technique based on an empirical The EMD served as an alternative signal processing technique based on an empirical and algorithm-defined method. EMD can adaptively decompose a complex signal into and algorithm-defined method. EMD can be used to decompose a signal without specifying the basis functions in advance; the degree of decomposition is adaptively determined by the basis in advance; the degree of decomposition is advantages adaptively determined by the naturefunctions of the signal to be decomposed. These are the main of EMD compared nature of the signal to be decomposed. Step 7 When r(t) satisfies the EMD stop criteria, set r(t) as the final residual signal, and terminate the whole process.
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