CN107085626A - A kind of vertical total electron content modeling method in region ionosphere merged based on BP multinomial models - Google Patents
A kind of vertical total electron content modeling method in region ionosphere merged based on BP multinomial models Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
- G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention discloses a kind of vertical total electron content modeling method in region ionosphere merged based on BP multinomial models, comprise the following steps：S1：Obtain the geographical latitude and longitude information of point of puncture and the vertical total electron content information in ionosphere of area observation point, and information during by the way that the data conversion obtained being obtained into poor solar hour angle, geomagnetic latitude and place；S2：Build multinomial model and the vertical total electron content information in ionosphere is modeled, obtain polynomial module offset and corresponding model residual values；S3：Build BP neural network error compensation model；S4：The vertical total electron content residual error in ionosphere being fitted using BP neural network error compensation model to multinomial model is forecast, so as to be compensated to multinomial model.The present invention can solve the problem that model error problem present in traditional multinomial model, improve the precision of the vertical total electron content modeling in region ionosphere, and stability is good.
Description
Technical field
The present invention relates to Global Navigation System field, more particularly to a kind of region merged based on BP- multinomial models
The vertical total electron content modeling method in ionosphere.
Background technology
During GNSS ionosphere modelings are studied and applied, two class ionospheric models are broadly divided into：One class is empirical model, including
Klobuchar models, IRI models, Bent models for being used in GPS etc., empirical model is more complicated and correction effect is poor, and one
As be not suitable in the modeling of high-precision ionosphere delay and GNSS positioning；Another kind of is to be based on high-precision GNSS Dual Frequency Observation number
According to the fitting ionospheric model of structure, according to the difference in modeling region, typically conventional ionospheric model includes following three：It is many
Item formula model (POLY), spheric harmonic function model (SHF), trigonometrical number (TSF) model.Researching and analysing, multinomial model fitting is residual
During poor changing rule, find to there is model error in multinomial model, it is therefore necessary to take effective error compensating method
To reduce or eliminate model error.
The content of the invention
Goal of the invention：Model error present in traditional multinomial model is can solve the problem that it is an object of the invention to provide one kind
The method of problem, to improve the precision of the vertical total electron content modeling in region ionosphere.
Technical scheme：To reach this purpose, the present invention uses following technical scheme：
The region ionosphere vertical total electron content modeling method of the present invention merged based on BP- multinomial models,
Comprise the following steps：
S1：The geographical latitude and longitude information of point of puncture and the vertical total electron content information in ionosphere of area observation point are obtained, and
Information during by the way that the data conversion obtained being obtained into poor solar hour angle, geomagnetic latitude and place；
S2：Build multinomial model and the vertical total electron content information in ionosphere is modeled, obtain multinomial model
Value and corresponding model residual values；
S3：Build BP neural network error compensation model；
S4：The vertical total electron content in ionosphere being fitted using BP neural network error compensation model to multinomial model is residual
Difference is forecast, so as to be compensated to multinomial model.
Further, in the step S1, the point of puncture geographic logitude and latitude information of area observation point pass through formula respectively
(1), formula (3) is obtained：
In formula (1), λiFor the point of puncture geographic logitude of area observation point, λ0For the geographic logitude of survey station receiver, ΨppFor
The earth's core subtended angle, as shown in formula (2), A is satellite aximuth,For the point of puncture geographic latitude of area observation point；
In formula (2), E is elevation of satellite, and R is earth radius, and H is the height equivlent of ionosphere single-layer model；
In formula (3),For the geographic latitude of survey station receiver.
Further, in the step S1, solar hour angle is poor, geomagnetic latitude and place when pass through formula (4), (5) and (6) respectively
Obtain：
Δ S=(λ-λ0)+(t-t0) (4)
In formula (4), Δ S is that solar hour angle is poor, and λ is point of puncture longitude, λ0For the geographic logitude of survey station receiver, t is sight
Survey moment point, t0To model the intermediate time point of period；
In formula (5),For geomagnetic latitude,For south geographical pole latitude,It is geographical for the point of puncture of area observation point
Latitude, λiFor the point of puncture geographic logitude of area observation point, λSFor south geographical pole S longitude；
τ=UTC+ λ/15 ° (6)
In formula (6), when τ is place, UTC is the Coordinated Universal Time(UTC).
Further, in the step S2, shown in multinomial model such as formula (7)：
In formula (7), VTEC is vertical total electron content, ai,jAnd b0kFor the undetermined coefficient of multinomial model, i=0~2, j
=0~2, k=1~5, Δ S is that solar hour angle is poor,For the difference of latitude of point of puncture distance areas center latitude,For earth magnetism latitude
Degree, when τ is place.
Further, in the step S3, BP neural network error compensation model is the Three Tiered Network Architecture of 6 × P × 1, its
In, 6 parameters of input layer are respectively：During the sun at the geographic latitude, geographic logitude, geomagnetic latitude, survey station at point of puncture
During place at angle, survey station and model value that multinomial model is calculated；Output layer is the residual error of polynomial module offset and true value.
Beneficial effect：The invention discloses a kind of vertical total electronics in region ionosphere merged based on BP- multinomial models
Content modeling method, multinomial model is combined with BP neural network, is intended using neutral net in nonlinear change data
Advantage in terms of conjunction is compensated to the error of multinomial model, can solve the problem that model error present in traditional multinomial model
Problem, improves the precision of the vertical total electron content modeling in region ionosphere, and stability is good.
Brief description of the drawings
Fig. 1 builds flow chart for the Fusion Model of the specific embodiment of the invention；
Fusion Models and the modeling effect contrast figure of traditional 2-DPM model of the Fig. 2 for the specific embodiment of the invention；
Fig. 3 is the 0 of the specific embodiment of the invention:00UTC moment ionosphere delay actual spatial distribution figures；
Fig. 4 is the 0 of the specific embodiment of the invention:The spatial distribution map of the correction deviation of 00UTC moment Fusion Models；
Fig. 5 is the 2 of the specific embodiment of the invention:00UTC moment ionosphere delay actual spatial distribution figures；
Fig. 6 is the 2 of the specific embodiment of the invention:The spatial distribution map of the correction deviation of 00UTC moment Fusion Models；
Fig. 7 is the 4 of the specific embodiment of the invention:00UTC moment ionosphere delay actual spatial distribution figures；
Fig. 8 is the 4 of the specific embodiment of the invention:The spatial distribution map of the correction deviation of 00UTC moment Fusion Models.
Embodiment
Technical scheme is further introduced with reference to the accompanying drawings and detailed description.
Present embodiment discloses a kind of region ionosphere merged based on BP- multinomial models, and vertically total electronics contains
Modeling method is measured, is comprised the following steps：
S1：The geographical latitude and longitude information of point of puncture and the vertical total electron content information in ionosphere of area observation point are obtained, and
Information during by the way that the information obtained conversion being obtained into poor solar hour angle, geomagnetic latitude and place；
S2：Build multinomial model and the vertical total electron content information in ionosphere is modeled, obtain multinomial model
Value and corresponding model residual values；
S3：Build BP neural network error compensation model；
S4：The vertical total electron content in ionosphere being fitted using BP neural network error compensation model to multinomial model is residual
Difference is forecast, so as to be compensated to multinomial model.
In step S1, the point of puncture geographic logitude and latitude information of area observation point are obtained by formula (1), formula (3) respectively：
In formula (1), λiFor the point of puncture geographic logitude of area observation point, λ0For the geographic logitude of survey station receiver, ΨppFor
The earth's core subtended angle, as shown in formula (2), A is satellite aximuth,For the point of puncture geographic latitude of area observation point；
In formula (2), E is elevation of satellite, and R is earth radius, and H is the height equivlent of ionosphere single-layer model；
In formula (3),For the geographic latitude of survey station receiver.
Solar hour angle is poor, geomagnetic latitude and place when obtained respectively by formula (4), (5) and (6)：
Δ S=(λ-λ0)+(t-t0)(4)
In formula (4), Δ S is that solar hour angle is poor, and λ is point of puncture longitude, λ0For the geographic logitude of survey station receiver, t is sight
Survey moment point, t0To model the intermediate time point of period；
In formula (5),For geomagnetic latitude,For south geographical pole latitude,It is geographical for the point of puncture of area observation point
Latitude, λiFor the point of puncture geographic logitude of area observation point, λSFor south geographical pole S longitude；
τ=UTC+ λ/15 ° (6)
In formula (6), when τ is place, UTC is the Coordinated Universal Time(UTC).
In step S2, shown in multinomial model such as formula (7)：
In formula (7), VTEC is vertical total electron content, ai,jAnd b0kFor the undetermined coefficient of multinomial model, i=0~2, j
=0~2, k=1~5, Δ S is that solar hour angle is poor,For the difference of latitude of point of puncture distance areas center latitude,For earth magnetism latitude
Degree, when τ is place.
In step S3, BP neural network error compensation model is the Three Tiered Network Architecture of 6 × P × 1, and the representation is many
Non-linear relation between item formula model residual delta VTEC and 6 input parameters.6 parameters of wherein input layer are respectively：Wear
When geographic latitude at thorn point, geographic logitude, geomagnetic latitude, the solar hour angle at survey station, place at survey station and multinomial model
The model value calculated；Output layer is the residual error of polynomial module offset and true value.
Below by taking one embodiment as an example, technical scheme is further introduced.
Embodiment 1：
Present embodiment discloses a kind of vertical total electron content modeling in region ionosphere merged based on BP- multinomial models
Method, comprises the following steps：
S11：Obtain the vertical total electronics of the geographical latitude and longitude information of point of puncture and the ionosphere at corresponding moment of area observation point
Content information, and by given data conversion obtain that solar hour angle is poor, geomagnetic latitude and it is local when etc. information；
The data source of use in the data of 73 CORS in the Jiangsu Province station days of year 323 of year in 2010 (November 19), according to
Survey station positional information, extracts related ionospheric data using carrier phase smoothed pseudorange method, that is, includes point of puncture (IPP) longitude and latitude
The information such as degree, observation time, VTEC values, concrete condition is shown in Table 1.
Because data volume is than larger, table 1 show only part modeling data.For the UTC on the same day of day of year 323 year of selection
Time is 0:00-4:4416 datas in 00 period, randomly select 3532 datas in the specific modelling phase and are built
Mould, remaining 884 data is used as inspection data.
The partial ionization of table 1 layer modeling data
S21：Build multinomial model and the vertical total electron content information data in ionosphere is modeled, obtain multinomial
Model value and corresponding model residual values；
S31：Build three layers of BP neural network error compensation model shown in accompanying drawing 1；
S41：The vertical total electron content residual error in ionosphere being fitted using BP neural network model to multinomial model is carried out
Forecast, so as to be compensated to multinomial model.
For the Fusion Model that newly puts forward and the predictability of traditional Complete Second Order multinomial model (2-DPM)
Can, after simulation terminates, its root-mean-square error (root mean square error, RMSE), absolute error is calculated respectively
(absolute error,Eabs), relative error (relative error, Erel), and coefficient correlation (correlation
coefficient,ρcor), specific formula is as follows：
In above-mentioned formula, N represents to carry out the data amount check of error analysis；VTECpredRepresent VTEC model predication value；
VTECtrueRepresent the VTEC true value for utilizing carrier phase smoothed pseudorange method to extract according to CORS data；WithPoint
Not Biao Shi VTEC predicted values average value and the average value of VTEC true value.
Table 2 gives accuracy comparison of the Fusion Model with 9-DPM models, in order to further illustrate that the fitting effect of fusion is excellent
In traditional 9-DPM models, according to 0:00UT-4:00UT time sequencings have chosen 111 inspection datas and depict two at equal intervals
The model bias comparison diagram of model is planted, as shown in Figure 2.Fig. 3-Fig. 8 gives 0:00UTC、2:00UTC (central instant), 4:
The spatial distribution of the correction deviation of three moment ionosphere delay actual spatial distribution situations such as 00UTC and corresponding Fusion Model
Situation.
The different model correction effects of table 2 compare
As can be seen from Table 2：In the inspection of Fusion Model and 2-DPM models error be respectively 1.106TECU,
1.291TECU, relative error is respectively 91.1%, 87.6%, and Fusion Model fitting precision is substantially better than traditional 2-DPM moulds
Type, 14.3% is improved compared with 2-DPM model accuracies.
Claims (5)
1. a kind of vertical total electron content modeling method in region ionosphere merged based on BP- multinomial models, it is characterised in that：
Comprise the following steps：
S1：The geographical latitude and longitude information of point of puncture and the vertical total electron content information in ionosphere of area observation point are obtained, and is passed through
The information when data conversion obtained is obtained into poor solar hour angle, geomagnetic latitude and place；
S2：Build multinomial model simultaneously the vertical total electron content information in ionosphere is modeled, obtain polynomial module offset and
Corresponding model residual values；
S3：Build BP neural network error compensation model；
S4：The vertical total electron content residual error in ionosphere being fitted using BP neural network error compensation model to multinomial model is entered
Row forecast, so as to be compensated to multinomial model.
2. the region ionosphere vertical total electron content modeling according to claim 1 merged based on BP- multinomial models
Method, it is characterised in that：In the step S1, the point of puncture geographic logitude and latitude information of area observation point pass through formula respectively
(1), formula (3) is obtained：
In formula (1), λiFor the point of puncture geographic logitude of area observation point, λ0For the geographic logitude of survey station receiver, ΨppFor the earth's core
Subtended angle, as shown in formula (2), A is satellite aximuth,For the point of puncture geographic latitude of area observation point；
In formula (2), E is elevation of satellite, and R is earth radius, and H is the height equivlent of ionosphere single-layer model；
In formula (3),For the geographic latitude of survey station receiver.
3. the region ionosphere vertical total electron content modeling according to claim 1 merged based on BP- multinomial models
Method, it is characterised in that：In the step S1, solar hour angle is poor, geomagnetic latitude and place when respectively by formula (4), (5) and
(6) obtain：
Δ S=(λ-λ0)+(t-t0) (4)
In formula (4), Δ S is that solar hour angle is poor, and λ is point of puncture longitude, λ0For the geographic logitude of survey station receiver, t is the observation moment
Point, t0To model the intermediate time point of period；
In formula (5),For geomagnetic latitude,For south geographical pole latitude,For the point of puncture geographic latitude of area observation point,
λiFor the point of puncture geographic logitude of area observation point, λSFor south geographical pole S longitude；
τ=UTC+ λ/15 ° (6)
In formula (6), when τ is place, UTC is the Coordinated Universal Time(UTC).
4. the region ionosphere vertical total electron content modeling according to claim 1 merged based on BP- multinomial models
Method, it is characterised in that：In the step S2, shown in multinomial model such as formula (7)：
In formula (7), VTEC is vertical total electron content, ai,jAnd b0kFor the undetermined coefficient of multinomial model, i=0~2, j=0~
2, k=1~5, Δ S are that solar hour angle is poor,For the difference of latitude of point of puncture distance areas center latitude,For geomagnetic latitude, τ
During for place.
5. the region ionosphere vertical total electron content modeling according to claim 1 merged based on BP- multinomial models
Method, it is characterised in that：In the step S3, BP neural network error compensation model is the Three Tiered Network Architecture of 6 × P × 1, its
In, 6 parameters of input layer are respectively：During the sun at the geographic latitude, geographic logitude, geomagnetic latitude, survey station at point of puncture
During place at angle, survey station and model value that multinomial model is calculated；Output layer is the residual error of polynomial module offset and true value.
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| CN110568458A (en) * | 2019-08-28 | 2019-12-13 | 桂林电子科技大学 | ionosphere VTEC closed-loop test system and method based on GNSS |
| CN111045046A (en) * | 2019-12-03 | 2020-04-21 | 东南大学 | Short-term ionosphere forecasting method and device based on NARX |
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| CN111985108A (en) * | 2020-08-22 | 2020-11-24 | 华东交通大学 | GNSS ionized layer TEC modeling method based on three-layer spherical harmonic approximation |
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| CN109855623B (en) * | 2019-01-09 | 2020-07-31 | 东南大学 | Online approximation method for geomagnetic model based on L egenderre polynomial and BP neural network |
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| CN110568458A (en) * | 2019-08-28 | 2019-12-13 | 桂林电子科技大学 | ionosphere VTEC closed-loop test system and method based on GNSS |
| CN111045046B (en) * | 2019-12-03 | 2021-10-19 | 东南大学 | Short-term ionosphere forecasting method and device based on NARX |
| CN111045046A (en) * | 2019-12-03 | 2020-04-21 | 东南大学 | Short-term ionosphere forecasting method and device based on NARX |
| CN111428746A (en) * | 2020-01-15 | 2020-07-17 | 北京航空航天大学 | Method for realizing ionosphere total electron content spatial feature extraction by using condition-generated countermeasure network |
| US20220308238A1 (en) * | 2020-07-13 | 2022-09-29 | Swift Navigation, Inc. | System and method for determining gnss positioning corrections |
| US11867823B2 (en) * | 2020-07-13 | 2024-01-09 | Swift Navigation, Inc. | System and method for determining GNSS positioning corrections |
| CN112034500A (en) * | 2020-08-20 | 2020-12-04 | 上海华测导航技术股份有限公司 | Regional grid ionosphere modeling method based on real-time PPP ambiguity fixing technology |
| CN111985108A (en) * | 2020-08-22 | 2020-11-24 | 华东交通大学 | GNSS ionized layer TEC modeling method based on three-layer spherical harmonic approximation |
| CN112862159A (en) * | 2021-01-13 | 2021-05-28 | 中铁第四勘察设计院集团有限公司 | Method, device, equipment and storage medium for predicting total electron content of ionized layer |
| CN114527497A (en) * | 2022-02-08 | 2022-05-24 | 国汽大有时空科技(安庆)有限公司 | Positioning enhancement information transmission method based on PPP-RTK |
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