Lightning electromagnetic pulse is strong electromagnetic interference sources, along with the rapid development of information technology, it has all kinds of hazards of electrical and electronic equipment, communications equipment becoming more serious; interference with normal radio communications and a variety of remote control devices, and become radio an important source of noise; secondly, it is also harmful to the area around the lightning channel, and in the case of a lightning strike electrical equipment may affect the normal operation of power systems. To protect sensitive equipment from the dangers of lightning electromagnetic pulse field and select the appropriate protective measures, the paper analyzes the situation of the surrounding space radiation when lightning electromagnetic pulse propagation, by solving the Maxwell equations, using Matlab to draw the amount of space each lightning electromagnetic field Distribution. Distribution by ground lightning electromagnetic pulse in the earth study law, can get a variety of electrical parameters conclude the lightning current, lightning charge moment and charge distribution in the cloud, etc..They can provide a viable reference for positioning data and lightning warning.
The full waveform decomposition technique is significant for LiDAR ranging. It is challenging to extract the parameters from non-Gaussian shaped waveforms accurately. Many parametric models (e.g. the Gaussian distribution, the lognormal distribution, the generalized normal distribution, the Burr distribution, and the skew-normal distribution) were proposed to fit sharply-peaked, heavy-tailed, and negative-tailed waveforms. However, these models can constrain the shape of the waveform components. In this article, the Gaussian convolution model is established. Firstly, a set of Gaussian functions is calculated to characterize the system waveform so that asymmetric and non-Gaussian system waveforms can be included. The convolution result of the system waveform and the target response is used as the model for fitting the overlapped echo. Then a combination method of the Richardson–Lucy deconvolution, layered iterative, and Gaussian convolution is introduced to estimate the initial parameters. The Levenberg–Marquardt algorithm is used for the optimization fitting. Through experiments on synthetic data and practical recorded coding LiDAR data, we compare the proposed method with two decomposition approaches (Gaussian decomposition and skew-normal decomposition). The experiment results revealed that the proposed method could precisely decompose the overlapped non-Gaussian heavy-tailed waveforms and provide the best ranging accuracy, component fitting accuracy, and anti-noise performance. However, the traditional Gaussian and skew-normal decomposition methods can not fit the components well, resulting in inaccurate range estimates.
This paper studies the basic structure and principles of adaptive noise filter proposed for the lightning current filtered LMS algorithm signal interference with the traditional difference is fixed filter. Development of adaptive filtering technology and the rapid development of VLSI technology for noise reduction study provides a theoretical and technical support. It is able to detect and extract the useful signal from the noise environment. LMS has the ability to automatically adjust its own parameters and it requires little or no prior knowledge of the undesired signal and noise. At same time, by means of MATLAB that lightning current signal by a double exponential model were to dry process simulation. Verification indicates that the filter has a good performance and this some practical value in the lightning current waveform analysis.
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