CN113361190B - CMONOC-ZTD-based troposphere delay correction method - Google Patents
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Abstract
The invention discloses a troposphere delay correction method based on CMONOC-ZTD, which comprises the following steps: s1: obtaining modeling data and constructing a training sample; s2: constructing a plurality of weak neural network models by using the samples obtained in the step S1; s3: inputting sample training in a 'weak' neural network model; s4: determining a weight for each "weak" neural network model; s5: and (5) carrying out weighted average on the results of the 'weak' neural network model, outputting a final model, and verifying the accuracy of the final model. The invention is superior to the common CTrop model and SHAtrop model, and the precision of the new model is improved by 9.6 percent and 3.3 percent compared with the two models; modeling is carried out by adopting a GNSS actually-measured ZTD sequence, and the method is different from other models, adopts meteorological analysis data or sounding data modeling, so that self-consistency of the model in GNSS application is realized to the greatest extent, and the application precision of navigation and positioning in a Chinese area can be ensured; the accuracy of the new model in inland and high altitude areas is higher than that of other commonly used models.
Description
Technical Field
The invention relates to a global navigation satellite system, in particular to a CMONOC-ZTD-based troposphere delay correction method.
Background
Tropospheric delay of a radio signal is one of important error sources affecting satellite navigation positioning accuracy, particularly accuracy in the elevation direction, and tropospheric zenith delay can generally reach 2m, whereas delay increases to 20m with decreasing altitude angle. The world-widely used zenith delay model with meteorological parameters mainly comprises Hopfield, saastamoinen, black models and the like, and the application of the model depends on the surface meteorological parameters, so that the application range of the model is greatly limited. The common model without meteorological parameters is built by adopting a numerical weather forecast model, and is a tropospheric delay model in the global scope built by analyzing global atmospheric average meteorological data and global climate. The model has poor precision, is difficult to realize self-consistent in GNSS navigation positioning, and has limited correction effect especially in areas with wide regions and complex environments. Currently, there are few ZTD models that are directly modeled based on GNSS-ZTD data. The method aims at the problems that the traditional troposphere zenith delay model is low in precision, poor in stability, dependent on meteorological parameters and the like in zenith delay estimation, and based on GNSS inversion troposphere zenith delay data provided by a Chinese regional land-state network, the self-adaptive learning capacity and the nonlinear approximation capacity of an artificial neural network are utilized, the defects of parameterized models in some traditional ZTD models are avoided, the optimal approximation effect is achieved through automatic adjustment of weights, an accurate and reliable troposphere delay model is built to achieve the effect of local refinement, the accuracy of the troposphere delay correction in the Chinese region is improved, and the method has important practical significance for improving the accuracy and reliability of GNSS navigation positioning.
Disclosure of Invention
The invention aims to: the invention aims to provide a high-precision CMONOC-ZTD-based troposphere delay correction method.
The technical scheme is as follows: the invention discloses a troposphere delay correction method based on CMONOC-ZTD, which comprises the following steps:
S1: obtaining modeling data and constructing a training sample;
S2: constructing a plurality of weak neural network models by using the samples obtained in the step S1;
s3: inputting sample training in a 'weak' neural network model;
S4: determining a weight for each "weak" neural network model;
s5: and (5) carrying out weighted average on the results of the 'weak' neural network model, outputting a final model, and verifying the accuracy of the final model.
The modeling data in the step S1 adopts a troposphere zenith delay (zenith total delay, ZTD) product (CMONOC-ZTD for short) sequence provided by a measuring station of a Chinese continental construction environment monitoring network (crustal movement observation network of China, CMONOC).
The "weak" neural network model in step S2 adopts the FNN network.
The feed-Forward Neural Network (FNN) network consists of an input layer, an hidden layer and an output layer, wherein the number of nodes of the input layer is 5, which respectively represent the longitude (degree), the latitude (degree), the altitude (m), the annual product day (dayofyear, DOY) and the hour (h) of a measuring station, the number of nodes of the hidden layer is 27, the number of nodes of the output layer is 1, which represents ZTD (mm), and the activation function selects a Tan-Sigmoid function, and the expression is as follows:
Wherein x is the input signal value of the neuron, f (x) is the output signal value, the target error of FNN is 1mm, the maximum training times are 200 times, and the training is performed by adopting a gradient descent method.
In the step S4, the weight of each "weak" neural network model is determined according to the training error calculation, and the distribution weight of m training samples is initialized as follows:
D0(i)＝1/m,i＝1,2,3,…,m
the initial accumulated error for each FNN network is:
Error(t)＝0,t＝1,2,3,…,20
After the training of the t-th FNN network is finished, if the prediction Error of the i-th sample obtained by the FNN t is larger than 50mm, the sample is considered to be the sample needing reinforcement learning, the accumulated Error (t) of the network and the distribution Error D t of the sample are updated according to the following formula, otherwise, the accumulated Error of the network and the distribution weight of the sample are unchanged;
the weight of each FNN network is calculated, and the weight p t of the t-th FNN network is calculated and normalized by the following formula:
Assuming that the tropospheric delay calculated by the t-th FNN network is denoted ZTD t, the final tropospheric delay is calculated by the following formula:
the verification accuracy in the step S5 is to take ZTD0 of the land network station not participating in the step S1 as a true value, and adopt average deviation (Bias) and root mean square error (Root mean square error, RMS) as evaluation criteria, where Bias represents accuracy, that is, the degree of deviation between the model and the true value, RMS represents accuracy, and is used for measuring reliability and stability of the model, and the calculation formula is as follows:
the beneficial effects are that: compared with the prior art, the invention has the following advantages:
1. Compared with the common CTrop model and SHAtrop model, the new model precision is improved by 9.6 percent and 3.3 percent;
2. modeling is carried out by adopting a GNSS actually-measured ZTD sequence, and the method is different from other models, adopts meteorological analysis data or sounding data modeling, so that self-consistency of the model in GNSS application is realized to the greatest extent, and the application precision of navigation and positioning in a Chinese area can be ensured;
3. The accuracy of the new model is higher in inland and high-sea areas than in other commonly used models.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a diagram of land network modeling, station verification, and information for an embodiment of the present invention;
FIG. 3 is a flowchart of an algorithm according to an embodiment of the present invention;
FIG. 4 shows the Bias and RMS distribution diagrams of each station of the model and CTrop, SHatrop model obtained by the method of this embodiment, wherein FIG. 4a is CTrop Bias,4b is SHAtrop Bias,4c is BP-Adaboost Bias,4d is CTrop RMS,4e is SHAtrop RMS, and 4f is BP-Adaboost RMS;
Fig. 5 shows the RMS comparison of the model obtained by the method according to this embodiment with CTrop, SHAtrop models in each quarter, where fig. 5a shows the spring models RMS,5b shows the summer models RMS,5c shows the autumn models RMS, and 5d shows the winter models RMS.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, this embodiment discloses a ZTD correction method based on COMONC, which includes the following steps:
S1: and obtaining and screening troposphere zenith delay products provided by a national continental construction environment monitoring network (land state network) station meeting the conditions. The method comprises the following steps:
In the specific embodiment, CMONC measuring stations with continuous data in the last 7 years are selected, and observed values with errors larger than 5mm in the measuring stations are removed, so that 217 measuring stations are obtained. And uniformly selecting 155 measuring stations 2014-2018 data for modeling, and verifying by using the rest 62 measuring stations (verification stations) and 141 measuring stations (modeling stations) which participate in modeling in 2019 data. 1883076 training samples are obtained, and 136958 and 271882 test samples are obtained respectively.
S2: and (3) constructing a plurality of weak neural network models by utilizing the samples obtained in the step (S1), determining that the input layer nodes are 5, and respectively representing the longitude (°), the latitude (°), the elevation (m), the annual product date (dayofyear, DOY) and the hour (h) of the measuring station, wherein the number of hidden layer nodes is 27, the number of output layer nodes is 1, and the number of hidden layer nodes represents an accurate ZTD (mm) predicted value.
S3: training establishes 20 FNN networks. Initializing the distribution weight of 1883076 training samples in total asEach FNN network has three layers, which are respectively composed of an input layer, an hidden layer and an output layer. The number of the input layer nodes is 5, and the input layer nodes respectively represent: measuring station longitude (°), latitude (°), elevation (m), annual product day (dayofyear, DOY) and hour (h); the hidden layer node number is 27; the number of output layer nodes is 1, representing ZTD (mm). The transfer function of the FNN model hidden layer selects a Tan-Sigmoid function, and the expression is shown in a formula (1), wherein x is an input value:
the target error of each FNN network is 1mm, the maximum iteration number is 200, and the training method adopts a standard steepest descent method.
S4: the method selects 20 FNN networks in total and integrates the weights of the FNN networks into a strong predictor for accurately estimating ZTD. The initial accumulated Error for each network (t) =0, t=1, 2,3, …,20. After the training of the t-th FNN (FNN t) network is finished, if the prediction Error of the i-th sample obtained by the FNN t is greater than 50mm, the sample is considered to be the sample which needs reinforcement learning, the accumulated Error (t) of the network and the distribution Error D t of the sample are updated according to the formula (2), otherwise, the accumulated Error of the network and the distribution weight of the sample are unchanged:
the weight p t of the t-th FNN network can be calculated and normalized by the formula (3):
s5: taking a weighted average of these 20 FNN network predictions as the final ZTD output, the formula is shown as (4), and determining the final improvement weight and verifying its accuracy.
The tropospheric delay of the two sets of stations in step S1 is therefore noted as ZTD 0 as the true value, using the mean deviation (Bias) and root mean square error (Root mean square error, RMS) as the evaluation criteria. Wherein Bias represents accuracy, i.e., the degree of deviation of the model from the true value; the RMS represents accuracy, and is used for measuring the reliability and stability of the model, and the calculation formula is shown in formula (5):
Wherein N represents the number of samples, ZTD i is the tropospheric delay calculated by the model, Is an accurate value of tropospheric delay. To discuss the applicability of the new model, ZTDs of 141 modeling stations and 62 verification stations of CMONOC in 2019 were used herein to compare the accuracy of the CTrop model and the SHAtrop model in the china area, respectively, and the specific results are shown in table 1; to further analyze the spatial applicability of each model, the Bias and RMS profiles of each CMONC station and sounding station are plotted as shown in fig. 4;
table 1: bias and RMS (units: mm) for four models
Further analyzing the applicability of the four models at different altitudes, according to the station elevation, respectively counting each station according to three intervals of 0-2km, 2-4km and >4km, as shown in table 2:
table 2: bias and RMS (units: mm) for two sub-regions
Further to verify the accuracy of the model for different time periods, the RMS of the four models at each quarter, respectively, is calculated as shown in fig. 5.
Table 3: bias and RMS (units: mm) for three elevation intervals
As can be seen from tables 1,2, 3, 4, and 5:
(1) The overall Bias of the BP-Adaboost model in the China area is 0.62mm and minus 1.16mm which are superior to the common CTrop model and SHAtrop model by utilizing 155 measuring stations participating in modeling in 2019 CMONOC and 62 measuring station data not participating in modeling, so that no obvious systematic error exists in the model; the RMS is 25.30mm and 26.72mm, and the precision is improved by 9.6%, 2.8% and 3.3% compared with the three models.
(3) The precision distribution of the three models shows the characteristic of high inland and low coastal area in southeast, and the advantage of the BP-Adaboost model in the inland area is more obvious. BP-Adaboost is also superior to the other three models in high altitude areas.
(4) The three models have low precision in summer and high precision in winter, and CMONOC sets of data verification results show that the precision of BP-Adbooost is improved by about 5% in spring and summer compared with those of CTrop models and SHAtrop models due to other models.
(5) The modeling is performed by adopting the GNSS actually-measured ZTD sequence, which is different from other models, adopts weather analysis data or Radiosonde data modeling, so that the self-consistency of the model in GNSS application is realized to the greatest extent, and the application precision of navigation and positioning in the China area can be ensured.
From the conclusion, the reliability of the modeling accuracy of the BP-Adaboost algorithm based on the land-state network of the China area is better than that of a model mainly used at present, both the deviation and the root mean square error are better, and meanwhile, the model is more excellent in performance in the land area of the China, so that the delay value of the land-state network of the China area can be calculated by using the method provided by the invention.
Claims (4)
1. A method for troposphere delay correction based on CMONOC-ZTD, comprising the steps of:
S1: obtaining modeling data and constructing a training sample;
S2: constructing a plurality of weak neural network models by using the samples obtained in the step S1;
The weak neural network model in the step S2 adopts a FNN network; the FNN network consists of an input layer, an hidden layer and an output layer, wherein the number of nodes of the input layer is 5, the nodes of the input layer respectively represent the longitude, latitude, elevation, annual product day and hour of a measuring station, the number of nodes of the hidden layer is 27, the number of nodes of the output layer is 1, the nodes of the output layer represent ZTD, and an activation function selects a Tan-Sigmoid function, and the expression is as follows:
wherein x is the input signal value of the neuron, f (x) is the output signal value, the target error of FNN is 1mm, the maximum training times are 200 times, and the training is performed by adopting a gradient descent method;
s3: inputting sample training in a 'weak' neural network model;
S4: determining a weight for each "weak" neural network model;
s5: and (5) carrying out weighted average on the results of the 'weak' neural network model, outputting a final model, and verifying the accuracy of the final model.
2. The correction method according to claim 1, wherein the modeling data in step S1 uses a tropospheric zenith delay product sequence provided by a station of a national continental construction environmental monitoring network.
3. The correction method according to claim 1, wherein the step S4 is performed by determining the weight of each "weak" neural network model according to the training error calculation, and initializing the distribution weights of m training samples as follows:
D0(i)＝1/m,i＝1,2,3,…,m
the initial accumulated error for each FNN network is:
Error(t)＝0,t＝1,2,3,…,20
After the training of the t-th FNN network is finished, if the prediction Error of the i-th sample obtained by the FNN t is larger than 50mm, the sample is considered to be the sample needing reinforcement learning, the accumulated Error (t) of the network and the distribution Error D t of the sample are updated according to the following formula, otherwise, the accumulated Error of the network and the distribution weight of the sample are unchanged;
the weight of each FNN network is calculated, and the weight p t of the t-th FNN network is calculated and normalized by the following formula:
Assuming that the tropospheric delay calculated by the t-th FNN network is denoted ZTD t, the final tropospheric delay is calculated by the following formula:
4. The correction method according to claim 1, wherein the verification accuracy in the step S5 is calculated by using ZTD 0 of the land network station not participating in the step S1 as a true value and using average deviation and root mean square error as evaluation criteria, wherein the calculation formula is:
Where N is the number of samples for verification accuracy, ZTD i is the tropospheric delay calculated by the model using the ith sample when verifying accuracy.
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