Research and Simulation of Negative Group Delay and Superluminal Propagation

In recent years, negative group delay circuits have attracted much attention due to their propagation characteristics and wide application prospects. In the history of human exploration, the exploration of the speed of light has never stopped. The theory of relativity points out that the speed of light in vacuum is the limit speed of signal propagation. However, it is found through research that phase velocity and group velocity appear faster than the speed of light, which does not violate the causal relationship. This paper first introduces the related concepts of negative group delay and superluminal phenomenon, the second focuses on the principle of negative group delay and superluminal phenomenon in-depth analysis and research, finally using the principle of Multisim software, the bandwidth of two different job, different structure of circuit design, the virtual simulation experiment to negative group delay phenomenon and measurement data. It is of great significance to explore the field of faster-than-light and negative group delay in today's rapidly developing information age, and it can try to meet the high requirements for signal transmission. In the future, the interdisciplinary research direction of this research topic also has great development space

Localized atmospheric density prediction method based on NARX neural network

Errors of orbit determination and prediction for low earth orbit (LEO) satellites mainly arise from the lack of accuracy in existing atmospheric density models. The lack of observation methods and insufficient understanding of physical mechanism of the upper atmosphere have brought difficulties to the modelling of atmospheric density. Two line element (TLE) was used to calibrate the MSIS atmospheric model, aiming at getting a localized density model along the orbit. Then a predictor was built based on the nonlinear autoregressive neural network with exogenous inputs (NARX). It uses calibrated MISIS model and a set of proxies of solar and geomagnetic activities to predict localized density values along the future orbit of a satellite. This model was applied for different types of satellite orbits and tested for different prediction windows. Comparison with the predictor based on the MSIS model shows a decrease in the mean error of the proposed model, which throws new light on improving the accuracy of LEO satellites’ short-time prediction