A method for ionospheric modeling based on the characteristics of ionospheric meridional variation

Abstract

The present invention discloses an ionosphere modeling method based on the ionosphere meridional variation characteristics, the method comprising the following steps: S01, obtaining CORS base station observation data in a strip area distributed along the meridian, estimating the ionosphere electron density information at the puncture point when different CORS base stations observe the same GNSS satellite at the same time according to the high-precision coordinates of the CORS base station and the high-precision satellite orbit, clock error and other information released by IGS; S02, grouping the ionosphere electron density information obtained in step S01 according to time, obtaining the ionosphere puncture point electron density information in the strip area arranged in time sequence, fitting the electron density information at each moment respectively to obtain the ionosphere electron density model at different moments in the strip area. This solves the problem that the prior art needs to establish dense CORS reference stations in order to obtain a high-precision ionosphere model, resulting in a waste of financial and material resources.

Description

Technical Field

[0001] The present invention relates to the technical field of ionosphere modeling, and in particular to an ionosphere modeling method based on the meridional variation characteristics of the ionosphere. Background Art

[0002] In the entire earth's sphere system, the ionosphere occupies a special position. There are enough free electrons here. The ionosphere and magnetosphere are closely coupled together through the electrodynamic coupling effect of large-scale field currents. The neutral components in the ionosphere are constantly ionized by solar radiation, and the charged oxygen ions and nitrogen ions continuously capture free electrons to synthesize neutral molecules. The balance of the two processes constitutes plasma. Solar radiation is the main factor affecting the activity state of the ionosphere, and the geomagnetic field also has a significant impact on the ionosphere. In the PPP positioning process, the ionospheric delay mainly affects the ambiguity fixation time. An accurate ionospheric delay parameter can reduce the ambiguity fixation time by several times and accelerate the convergence of the positioning results. In order to obtain the ionospheric delay information on the GNSS signal propagation path in real time, the ionospheric delay parameters broadcast by the CORS system are widely used to improve the positioning convergence time, and the current position is interpolated in real time using the TEC information broadcast in real time by the fixed reference stations around the mobile station. However, in order to obtain a high-precision ionospheric model, it is usually necessary to establish dense CORS base stations, resulting in a waste of financial and material resources. Summary of the invention

[0003] In order to solve the above shortcomings and deficiencies of the prior art, the object of the present invention is to provide an ionospheric modeling method based on the meridional variation characteristics of the ionosphere.

[0004] The technical solution of the present invention is: an ionosphere modeling method based on the ionosphere meridional variation characteristics, the method comprising the following steps:

[0005] S01. Obtain the CORS base station observation data in the strip area distributed along the longitude, and estimate the ionospheric electron density information at the puncture point when different CORS base stations observe the same GNSS satellite at the same time based on the high-precision coordinates of the CORS base station and the high-precision satellite orbit and clock information released by IGS;

[0006] S02, grouping the ionospheric electron density information obtained in step S01 according to time to obtain the electron density information of the ionospheric puncture points in the strip region arranged in time sequence, and performing adaptive indefinite-order polynomial fitting on the electron density information at each moment to obtain the ionospheric electron density model at different moments in the strip region.

[0007] Furthermore, it also includes:

[0008] S03. Obtain historical ionospheric data in the strip area, use polynomial fitting or LSTM neural network to calculate the ionospheric information at the current moment, and perform weighted averaging with the ionospheric model result at the current moment to obtain the final parameters of the model at the current moment.

[0009] The beneficial effects of the present invention are: compared with the prior art,

[0010] 1) The present invention obtains the electron density information of the ionospheric puncture points in the strip area arranged in time sequence by using the CORS station data in the strip area distributed along the meridian and the GNSS space environment monitoring algorithm, and fits the electron density information at each moment to obtain the ionospheric electron density model at different moments in the strip area. The present invention analyzes the ionospheric distribution characteristics, realizes that in the case of long-distance and sparsely distributed stations of the CORS system, a high-precision ionospheric electron density model can still be obtained, and the layout of base station construction is reasonably planned, which greatly increases the base station spacing and reduces the number of base stations;

[0011] 2) The present invention provides historical data to correct the ionospheric electron density model at different times in the strip region, thereby obtaining a more accurate ionospheric electron density model at different times in the strip region. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of Example 1 of the present invention;

[0013] Figure 2 This is a schematic diagram of satellite signal puncture points in implementation example 1 of the present invention;

[0014] Figure 3 The figure shows the accuracy comparison between this model and GIM. DETAILED DESCRIPTION

[0015] The invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0016] Implementation Example 1: Reference Figure 1 to Figure 2 , an ionosphere modeling method based on the ionosphere meridional variation characteristics, the method comprising the following steps:

[0017] S01. Obtain the CORS base station observation data in the strip area distributed along the longitude, and estimate the R of different CORS base stations based on the high-precision coordinates of the CORS base stations and the high-precision satellite orbits and clock errors published by IGS. i When observing the same GNSS satellite S at the same time t, the ionospheric electron density information at the puncture point is obtained

[0018] S02, the ionospheric electron density information obtained in step S01 Grouping by time, obtaining the electron density information of the ionospheric puncture points in the time-series band area The electron density information at each moment The ionospheric electron density models at different times in the strip region are respectively fitted.

[0019] Since the changes in the ionosphere are mainly affected by the modulation of solar activity and geomagnetic activity, the regularity in the longitude direction is stronger than that in the latitude direction. The present invention obtains the electron density information of the ionosphere puncture points in the strip area arranged in time sequence by using the CORS station data and GNSS space environment monitoring algorithm in the strip area distributed along the longitude, and fits the electron density information at each moment to obtain the ionosphere electron density model at different moments in the strip area. The present invention analyzes the distribution characteristics of the ionosphere, realizes that in the case of long-distance and sparsely distributed stations of the CORS system, a high-precision ionosphere electron density model can still be obtained, and the layout of base station construction is reasonably planned, which greatly increases the distance between base stations and reduces the number of base stations.

[0020] The present invention is further explained below in conjunction with charts and specific measured data. The data was selected from the American continent, and 9 stations distributed along the meridians (P392, P738, P145, GARL, P127, P143, P512, P566, P563) were selected. The data period is from the 12th to the 18th of each month in 2014, and the three periods of UT07:00-UT09:00, UT13:00-UT15:00, and UT17:00-UT19:00 every day.

[0021] The first step is to select the appropriate satellite observation period based on the above site observation data and obtain the ionospheric electron content information of the satellite signal penetration point. Table 1 shows the STEC data file fragment of the GNSS satellite penetration point in a certain period.

[0022]

[0023] Table 1: STEC data file fragment of GNSS satellite penetration point in a certain period

[0024] According to the data files in the table, the observation results can be classified according to the satellite number, and the ionospheric electron density information of the observation signal penetration point of the same satellite by different base stations can be obtained. Table 2 shows the result data file of the CORS base station observing the GPSPRN04 satellite in a certain period of time.

[0025]

[0026] Table 2: A snippet of the CORS base station's observation results for PRN04.

[0027] The second step is to collect all the observation data in the modeling area, classify them, and then fit the polynomial model to the data classification results. The mathematical expression of the model is as follows:

[0028] F(x)=P0+P1·x+P2·x 2 +…+P n ·x n

[0029] In the fitting process, the historical data and historical models of the area are first used as references, and the 5-fold cross-validation method is used to determine the order of the polynomial model. After the order is determined, the observation data of the current period is used to obtain the parameters of the polynomial model. This model can provide GNSS positioning with high-precision ionospheric electron density information of the desired point.

[0030] Furthermore, it also includes: step S03, obtaining historical ionospheric data in the strip area, using polynomial fitting or LSTM (Long Short-Term Memory) neural network to infer the ionospheric information at the current moment, and performing weighted averaging with the ionospheric model result at the current moment to obtain the final parameters of the model at the current moment.

[0031]

[0032] Table 3: Corrected model parameters

[0033] The figure below shows the model accuracy statistics for the whole year of 2014 in the strip area (red curve) and the model accuracy statistics for the ionospheric products (GIM, Global Ionosphere Map) released by the International GNSS Service (IGS) in the same area (blue curve). Mean is the mean of model accuracy, and RMS is the root mean square error of model accuracy. It can be seen that the accuracy of this model is higher than that of the GIM model during the statistical period of the whole year of 2014, which is reflected in the fact that the error statistics of this model results are smaller than those of the GIM model in all months, especially in summer. The mean error is reduced by 113% in the worst case, and the RMS is reduced by 108%.

[0034] After using this model to correct the ionospheric information, the advantage is that it can assist the positioning results, shorten the convergence time of PPP positioning and improve the accuracy of PPP positioning results.

[0035] Figure 3 The figure shows the accuracy comparison between this model and GIM. The lines marked with * are the results of this model, and the lines marked with ■ are the results of GIM.

[0036] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. An ionosphere modeling method based on the ionosphere meridional variation characteristics, characterized in that: The method comprises the following steps: S01. Obtain the CORS base station observation data in the strip area distributed along the longitude, and estimate the ionospheric electron density information at the puncture point when different CORS base stations observe the same GNSS satellite at the same time based on the high-precision coordinates of the CORS base station and the high-precision satellite orbit and clock information released by IGS; S02, grouping the ionospheric electron density information obtained in step S01 according to time to obtain the electron density information of the ionospheric puncture points in the strip region arranged in time sequence, and performing adaptive indefinite-order polynomial fitting on the electron density information at each moment to obtain the ionospheric electron density model at different moments in the strip region; Nine stations along the meridians were selected (P392, P738, P145, GARL, P127, P143, P512, P566, P563). The data period is from the 12th to the 18th of each month in 2014, from UT07:00 to UT09:00 and UT13:00 to UT15:00, UT17:00-UT19:00; Based on the observation data of the above stations, the ionospheric electron content information of the satellite signal puncture point is obtained; After collecting and classifying all the observation data in the modeling area, the data classification results are fitted with a polynomial model. The mathematical expression of the model is as follows: F(x)=P0+P1·x+P2·x 2 +…+P n ·x n In the fitting process, the historical data and historical models of the area are first used as references, and the 5-fold cross-validation method is used to determine the order of the polynomial model. After the order is determined, the observation data of the current period is used to obtain the parameters of the polynomial model. This model can provide GNSS positioning with high-precision ionospheric electron density information of the desired point.

2. The ionosphere modeling method based on the ionosphere meridional variation characteristics according to claim 1, characterized in that: The method also includes: step S03, obtaining historical ionospheric data in the strip area, using polynomial fitting or LSTM neural network to infer the ionospheric information at the current moment, and performing weighted averaging with the ionospheric model result at the current moment to obtain the final parameters of the model at the current moment.

Images