CN104992054B - The vertical total electron content forecasting procedure in ionosphere based on time series two dimension - Google Patents
The vertical total electron content forecasting procedure in ionosphere based on time series two dimension Download PDFInfo
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Abstract
The invention discloses the vertical total electron content forecasting procedures in ionosphere based on time series two dimension, including four steps：Step S1 is changed with time characteristic by the vertical total electron content in known ionosphere vertical total electron content data analysis ionosphere；Step S2 is structure time series two dimension plane, determine the weights between the vertical total electron content in ionosphere to be predicted total electron content vertical with known ionosphere, and according to weights to preparing to be weighted processing as the vertical total electron content data in known ionosphere of input layer；Step S3 is structure neural network model；Step S4 is forecast to the vertical total electron content in ionosphere using neural network model.The present invention carries out the processing of time series two dimensionization to the vertical total electron content in ionosphere, is arranged using differential weights value, has the good value of forecasting to the vertical total electron content in ionosphere.
Description
Technical field
The present invention relates to the vertical total electron content forecasting procedures in ionosphere, more particularly to based on time series two dimension
The vertical total electron content forecasting procedure in ionosphere.
Background technology
It is influenced by solar ultraviolet, ray radiation and high energy particle, in 60-2000km air layer regions, is existed big
The free electron of amount forms earth ionosphere.Measurement essence of the ionosphere to satellite positioning, navigation, time service and remote sensing, telemetering etc.
Spend important, therefore monitoring, forecast and the existing reporting system of ionosphere parameter be by domestic and foreign scholars' most attention, also
It is included in one of China's Space physical study Strategy Contents.PROGRESS OF IONOSPHERIC RESEARCH IN is ensureing radio communication, radio and television, over the horizon thunder
Up to etc. systems reliability service, improve test the speed, position, time service, the systems such as navigation precision, be to ensure space flight to a certain extent
Movable safety develops and uses space and safeguards that the living environment of the mankind provides foundation etc. and has important value.
Ionosphere delay has the measurement accuracy such as GPS positioning, navigation, time service, remote sensing important influence.Although double frequency
GPS user can directly calculate Ionospheric delay correcting, but due to the use of cost height, be still inclined to there are many field people in practice
In selection single-frequency GPS receiver.And prolonged using single-frequency GPS progress Long baselines relative measurement and high-precision absolute measurement, ionosphere
Slow accurate correction is difficult to realize.In high precision, Long baselines relative positioning, and static quick, real-time and dynamic relative positioning, soon
Fast accurate processing integer ambiguity problem, Ionospheric delay correcting is the wherein main and most thorny issue, and research
Centrostigma.The Systematic Errors brought by the stronger ionosphere delay of local correlations can directly give by relative positioning technology
To offset.But it is guaranteed in the short baseline calculating that its effect can only be under normal operation, and to Long baselines, difference ionosphere is prolonged
Slow residual error is generally very big.If ionospheric forecast problem is preferably solved, the high-precision correction ionization in other measurement methods
The requirement of layer delay also will all be met.Using the ionosphere delay information inverting ionospheric structure of GPS, analyze space-variant at that time
Change feature, detection and prediction ionosphere activity and changing rule, to ionosphere VTEC carry out accurately forecast be must continue into
Capable research puzzle and very meaningful.
The forecast experience model in ionosphere mainly have Klobuchar models, Bent models, IRI models, ICED models and
FAIM models etc., the most commonly used is ARIMA models and Klobuchar models, but its forecast precision is not high, pre- to night
There are irrationalities for report.Ionosphere be at any time, space and the changeable medium changed, Day-to-day variability are widely different, to not
Carry out several hours, several days ionosphere is made and accurately forecasts to be more difficult.Ionosphere is influenced by various factors, with week
Day, season, anniversary and solar cycle variation and change, be that one is the observations with apparent time behavior
Amount, therefore analyze it the research direction that forecast is current using time series.
Invention content
Goal of the invention：The object of the present invention is to provide a kind of good ionospheres based on time series two dimension of value of forecasting
Vertical total electron content forecasting procedure.
Technical solution：To reach this purpose, the present invention uses following technical scheme：
The vertical total electron content forecasting procedure in ionosphere of the present invention based on time series two dimension, including it is following
The step of：
S1：At any time by the vertical total electron content in known ionosphere vertical total electron content data analysis ionosphere
Variation characteristic, including the vertical total electron content in ionosphere with the vertical total electron content of the variation characteristic on date and ionosphere with
The variation characteristic at moment；
S2：According to the vertical total electron content data in known ionosphere in step S1, made respectively using date and hour
For x-axis and y-axis, time series two dimension plane is built, each coordinate points both corresponds to one in time series two dimension plane
The vertical total electron content data in a ionosphere；It is changed with time spy according to the vertical total electron content in ionosphere in step S1
Property, determine the weights between the vertical total electron content in ionosphere to be predicted total electron content vertical with known ionosphere, and
According to weights to preparing to be weighted processing as the vertical total electron content data in known ionosphere of input layer；
S3：Using the vertical total electron content data in ionosphere by step S2 weighting processing as input layer, using waiting for
The vertical total electron content data in ionosphere of prediction build neural network model as output layer；
S4：The vertical total electron content in ionosphere is forecast using neural network model.
Further, the weight w in the step S2i,jFor：
Weighting processing procedure in the step S2 is：
Wherein, (m, n) is the vertical total electron content data in ionosphere to be predicted in the time series two dimension plane
In coordinate, (i, j) be it is described prepare as input layer the vertical total electron content data in known ionosphere in the time
Coordinate in sequence two dimension plane, di,jFor the point that point and coordinate that coordinate in the two dimensionization plane is (m, n) are (i, j)
The distance between, xi,jThe vertical total electron content data in known ionosphere for the preparation as input layer,It is described
It is weighted the vertical total electron content data in ionosphere of processing.
Advantageous effect：The present invention by the vertical total electron content time series in ionosphere by carrying out two dimensionization processing, to electricity
The vertical total electron content of the absciss layer characteristic that changes with time has carried out sufficient excavation；Present invention employs the setting of differential weights value,
Ionosphere VTEC data distance preparation i.e. to be predicted is remoter as the known ionosphere VTEC data of input layer, then weights are got over
Small, the effect of this weights is better than equal weights；The present invention is to the value of forecasting of the vertical total electron content in ionosphere better than existing
Technology.
Description of the drawings
Fig. 1 is that the vertical total electron content in 28 days ionosphere of the specific embodiment of the invention observes data；
Fig. 2 is that the vertical total electron content in preceding 7 days ionosphere of the specific embodiment of the invention changes with time characteristic；
Fig. 3 is that the vertical total electron content in ionosphere is special with the variation on date in the same time for the phase of the specific embodiment of the invention
Property；
Fig. 4 is the time series two dimension plane of the specific embodiment of the invention and vertically total electronics contains in corresponding ionosphere
The 3-D graphic that amount data collectively form；
Fig. 5 is the Artificial Neural Network Structures figure of the specific embodiment of the invention；
Fig. 6 is the comparison of the forecast result and actual observation data of the method for the present invention.
Specific implementation mode
Technical scheme of the present invention is further introduced With reference to embodiment.
The method of the present invention includes following steps：
S1：At any time by vertical total electron content (abbreviation VTEC) the data analysis ionosphere VTEC in known ionosphere
Variation characteristic, including ionosphere VTEC with the date variation characteristic and ionosphere VTEC with the moment variation characteristic.
In order to study the value of forecasting of ionosphere amphibolic stage VTEC, it is contemplated that the authority and precision of initial data,
Using the station IGS SHAO in ionosphere activity peak year in the present embodiment, i.e., 2011 year days of year the 55-82 days totally 28 days
Data are analyzed.Data time series are observed using the survey station zenith direction VTEC that GAMIT softwares are calculated, such as Fig. 1 institutes
Show, the sampling interval is 1 hour.9.0 grades of 7.3 grades of violent earthquakes of Richter scale (on March 9th, 2011) and Richter scale occur respectively for Japan during this
Special violent earthquake (on March 11st, 2011), it can be seen that shaking preceding ionosphere has anomalous variation, the disturbance in ionosphere on daytime and night
It is relatively more with irregular variation.
When analysis ionosphere VTEC changes with time characteristic, preceding 7 days data are selected, as shown in Fig. 2, and calculating
Between daily VTEC data coefficient R (such as：I-th day R refers to i-th day VTEC data and (i-1)-th day VTEC
Related coefficient between data).From the point of view of the value (being all in close proximity to 1) of each R, ionosphere VTEC data with the date variation
Rule has obviously periodically (period is 1 day).
In addition, in order to preferably analyze variation characteristics of the ionosphere VTEC with the moment, this 28 days are given here at four
The data at moment (the 7th hour, the 8th hour, the 16th hour and the 17th hour), as shown in figure 3, horizontal axis represents date, longitudinal axis generation
The VTEC at the table moment accounts for the ratio of this day VTEC accumulated value.From figure 3, it can be seen that the VTEC of same date in the same time is not opposite
Size only has fluctuation slightly, and in most cases fluctuation is no more than 1%, that is to say, that the not VTEC of same date in the same time
With relatively apparent correlation.
In summary analysis is it is found that the VTEC data on adjacent date are very close, and the not VTEC of same date in the same time
Data fluctuations very little.
S2：According to the known ionosphere VTEC data (i.e. 28 days VTEC data) in step S1, using the date and when
It carves respectively as x-axis and y-axis, builds time series two dimension plane, each coordinate points in time series two dimension plane
Corresponding to an ionosphere VTEC data, as shown in figure 4, can determine certain using two dimensions (certain date, certain moment) in this way
A unique VTEC values；It is changed with time characteristic according to the ionosphere VTEC in step S1, determines ionosphere to be predicted
Weights between VTEC and known ionosphere VTEC, and according to weights to preparing the known ionosphere VTEC as input layer
Data are weighted processing, and preceding 3 days VTEC data are weighted after processing and are used as input layer by the present embodiment.According to step S1
It is found that the VTEC data on adjacent date are very close, and therefore the VTEC data fluctuations very littles of same date in the same time do not carry
Go out a kind of weighting scheme：In time series two dimension plane, ionosphere VTEC data distance to be predicted prepares as input
The known ionosphere VTEC data of layer are remoter, then weights are smaller namely weights are inversely proportional with distance.Weight wi,jSuch as following formula：
Weighting processing procedure is：
Wherein, (m, n) is the coordinate of ionosphere VTEC data to be predicted in time series two dimension plane, (i, j)
For coordinate of the known ionosphere VTEC data in time series two dimension plane for preparing as input layer, di,jFor two dimension
Change the point and the distance between the point that coordinate is (i, j), x that coordinate in plane is (m, n)i,jTo prepare as known to input layer
Ionosphere VTEC data,To be weighted the ionosphere VTEC data of processing.
S3：It is used as input layer after first 3 days VTEC data are weighted processing in the present embodiment, that is, there are 192 inputs
Layer；Hidden layer number is set as 50；Using the VTEC data of extrapolation 1 day as output data, i.e., there are one output layer, structure nerves
Network model, as shown in Figure 5.This neural network model is trained with 28 days VTEC data, learning method uses under gradient
Drop method, learning process are simulated with matlab Neural Network Toolbox.
S4：The vertical total electron content in ionosphere is forecast using neural network model.
The present embodiment can forecast the 4th day VTEC data by preceding 3 days VTEC data, and so on, extremely by i-th day
The i-th+2 days VTEC data can forecast the i-th+3 days VTEC data, can obtain follow-up 5 to 28 days 496 hours in this way
VTEC forecast results, as shown in Figure 6.As it can be seen that the forecast data of the present invention and the degree of agreement of observation data are preferable.
Currently, being ARIMA models to the ionosphere VTEC common models predicted, it is a kind of utilization parameter model pair
Orderly random data is handled, the method to carry out Modal Parameter Identification.VTEC is carried out using ARIMA models to give the correct time in advance,
If a certain period ionosphere is more stable, forecast is easier to reach preferable effect；But if a certain period ionosphere
Comparison of Gardening Activities is strong, and the irregular variation of VTEC time serieses and randomness will be bigger, using the value of forecasting of ARIMA models
With regard to poor, and the forecast precision of ARIMA models with extrapolation the time growth can be deteriorated rapidly.
The method of the present invention is compared with the forecast result of ARIMA models below, using identical data, is used respectively
The method of the present invention and ARIMA models are extrapolated to VTEC one day and are forecast, using average deviation BIAS and middle error RMSE as
The basic standard of model comparative analysis verification, their calculating formula are respectively：
Wherein：N is the number of days of ionosphere VTEC data；VTECcFor calculated value, VTEC is that true value (passes through IGS data meters
The VTEC data of calculation).The accuracy comparison of two kinds of forecasting procedures is as shown in table 1.
The comparison of the forecast result of 1 the method for the present invention of table and ARIMA models
| Method | Residual error | BIAS | RMSE |
| The method of the present invention | 0.11 | 2.10 | 2.78 |
| ARIMA | 0.73 | 2.34 | 3.29 |
From table 1 it follows that relative to ARIMA models, VTEC forecast precisions can be improved 15% by the method for the present invention
Left and right.
Claims (1)
1. the vertical total electron content forecasting procedure in ionosphere based on time series two dimension, it is characterised in that：Including below
Step：
S1：It is changed with time by the vertical total electron content in known ionosphere vertical total electron content data analysis ionosphere
Characteristic, including the vertical total electron content in ionosphere is as the vertical total electron content of the variation characteristic on date and ionosphere is with the moment
Variation characteristic；
S2：According to the vertical total electron content data in known ionosphere in step S1, using date and hour respectively as x-axis
And y-axis, time series two dimension plane is built, each coordinate points both corresponds to an ionization in time series two dimension plane
The vertical total electron content data of layer；It is changed with time characteristic, is determined according to the vertical total electron content in ionosphere in step S1
Weights between the vertical total electron content in ionosphere to be predicted total electron content vertical with known ionosphere, and according to weights
To preparing to be weighted processing as the vertical total electron content data in known ionosphere of input layer；
Weight w in the step S2i,jFor：
Weighting processing procedure in the step S2 is：
Wherein, (m, n) is the vertical total electron content data in ionosphere to be predicted in the time series two dimension plane
Coordinate, (i, j) prepare the vertical total electron content data in known ionosphere as input layer in the time series to be described
Coordinate in two dimensionization plane, di,jBetween the point that coordinate in the two dimensionization plane is (m, n) and the point that coordinate is (i, j)
Distance, xi,jThe vertical total electron content data in known ionosphere for the preparation as input layer,For the process
Weight the vertical total electron content data in ionosphere of processing；
S3：Using the vertical total electron content data in ionosphere by step S2 weighting processing as input layer, use is to be predicted
The vertical total electron content data in ionosphere as output layer, build neural network model；
S4：The vertical total electron content in ionosphere is forecast using neural network model.
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| CN105808821A (en) * | 2016-02-25 | 2016-07-27 | 东南大学 | Short term forecasting method of vertical total electron content of ionized layer on the basis of EOF (Empirical Orthogonal Function) decomposition |
| CN106096311B (en) * | 2016-06-24 | 2018-06-22 | 南京信息工程大学 | A kind of ionosphere VTEC value method for detecting abnormality |
| CN111428746B (en) * | 2020-01-15 | 2021-02-12 | 北京航空航天大学 | Method for realizing ionosphere total electron content spatial feature extraction by using condition-generated countermeasure network |
| CN111539453B (en) * | 2020-03-30 | 2022-04-26 | 东南大学 | Global ionized layer electron total content prediction method based on deep cycle neural network |
| CN111539455B (en) * | 2020-03-30 | 2022-04-26 | 东南大学 | Global ionosphere electron total content prediction method based on image primary difference |
| CN112862159B (en) * | 2021-01-13 | 2023-04-28 | 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455702A (en) * | 2012-11-28 | 2013-12-18 | 东南大学 | Method for determining regional ionospheric layer delay |
| CN104090280A (en) * | 2014-06-04 | 2014-10-08 | 郑州轻工业学院 | Ionosphere delay correction forecasting method based on regional CORS |
-
2015
- 2015-06-19 CN CN201510344370.1A patent/CN104992054B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455702A (en) * | 2012-11-28 | 2013-12-18 | 东南大学 | Method for determining regional ionospheric layer delay |
| CN104090280A (en) * | 2014-06-04 | 2014-10-08 | 郑州轻工业学院 | Ionosphere delay correction forecasting method based on regional CORS |
Non-Patent Citations (2)
| Title |
|---|
| 区域电离层VTEC的监测和短期预报模型研究;华远峰;《万方数据库》;20141230;第22-62页 * |
| 用于中国地区电离层总电子含量短期预报方法;刘瑞源，等;《电波科学学报》;20110228;第26卷(第1期);第18-24页 * |
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