Vertical measurement ionogram inversion method integrated with data pre-processing

Abstract

The invention discloses a vertical measurement ionogram inversion method integrated with data pre-processing. The method comprises the following steps: step A, establishing a multi-term ionized layer model; step B, based on the established ionized layer model and in combination with actually-measured virtual height data, obtaining coefficients of the multi-term ionized layer model through a search and iteration method under a restrain condition that a section is continuous and smooth so as to carry out extrapolation compensation preprocessing on missed actually-measured data; step C, setting actual heights h1 and h2 corresponding to frequencies f1 and f2, and calculating an average group refraction index and an overlapping multi-term coefficient based on the continuous pre-processed virtual height data, thereby calculating the actual heights when fii is equal to 3,4,5,...,n-1; step D, calculating the actual height hn corresponding the maximal frequency fn; and step E, calculating a peak height of the ionized layer.

Description

technical field [0001] The invention relates to the field of ionospheric research and application, in particular to a vertical ionogram inversion method for fusion data preprocessing. Background technique [0002] The inversion of the vertical ionogram is of great significance to the study of the structure of the ionosphere and the propagation of ionospheric waves. It is widely valued, but inversion is quite difficult. At present, vertical ionogram inversion methods can be summarized into two types: one is the direct calculation method, which calculates the true height of the ionosphere directly from the measured false height according to the corresponding relationship between the true height of the ionosphere and the false height; the other The first is the model method, which assumes that the ionospheric profile can be characterized by a certain model, and determines the ionospheric profile by finding the model parameters that make the vertical traces synthesized based on...

AI Technical Summary

Problems solved by technology

Because this method is directly based on the actual detection data, the data quality has a great influence on its accuracy. A small amount of false high data missing will directly lead to the oscillation of the calculated profile, and a large amount of data missing will cause a large deformation and displacement of the profile. Detection equipment and ionospheric fading, the lack of actual detection false high data is inevitable

In addition, some direct interpolation methods for detecting false height data do not combine the propagation characteristics of the ionosphere, and can perform better interpolation for a small amount of data missing near adjacent frequencies in non-layers, but for more or a large number of data missing and each layer The lack of data near adjacent frequencies may result in completely wrong interpolation results, which further increases the calculation error of the profile

Images