CN111323795B - Multi-path error weakening method in Beidou deformation monitoring - Google Patents

Multi-path error weakening method in Beidou deformation monitoring Download PDF

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CN111323795B
CN111323795B CN202010200531.0A CN202010200531A CN111323795B CN 111323795 B CN111323795 B CN 111323795B CN 202010200531 A CN202010200531 A CN 202010200531A CN 111323795 B CN111323795 B CN 111323795B
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CN111323795A (en
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梁晓东
雷孟飞
孔超
杨振武
周俊华
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Hunan Lianzhi Monitoring Technology Co ltd
Hunan Lianzhi Technology Co Ltd
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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
    • G01S19/13—Receivers
    • G01S19/22—Multipath-related issues
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
    • G01B15/06—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons for measuring the deformation in a solid
    • 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position

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Abstract

The invention provides a method for weakening multi-path errors in Beidou deformation monitoring, which comprises five steps of acquiring multi-path errors, establishing a model, correcting errors, judging correlation coefficients and updating the model, wherein the acquired multi-path errors mainly comprise the steps of removing high-frequency signals in monitoring data and the moving trend of monitoring points, and then extracting the multi-path errors through curve fitting; the model building step is mainly used for building a multi-path error model for monitoring data of multiple days and outputting model parameters according to a specified format; the error correction step is mainly to compare the fitted monitoring data after the processing of the monitoring data to be corrected with the optimal interpolation data in the multipath error model to obtain a correlation coefficient; and then judging the correlation coefficient and updating the model. The method can directly weaken the influence of multipath errors in the data processing process (namely in the resolving process); the error correction of the invention adopts the monitoring data of one day, the model is updated in time, and the monitoring precision is improved.

Description

Multi-path error weakening method in Beidou deformation monitoring
Technical Field
The invention relates to the technical field of Beidou satellite navigation, in particular to a method for weakening a multipath error in Beidou deformation monitoring.
Background
The BeiDou Navigation Satellite System (BDS) is a global Navigation Satellite System independently developed in our country. Compared with the American GPS, the Beidou navigation system satellite has the advantages of high altitude, strong signal, multiple frequency bands and the like. With the deployment of the Beidou satellite system III, the Beidou technology is widely applied to various fields such as navigation, deformation monitoring, positioning, time service and the like.
With the gradual networking of the Beidou satellite navigation system, the Beidou satellite system is more and more widely applied to deformation monitoring in houses, bridges and side slopes. The Beidou deformation monitoring precision is related to a Beidou receiver on one hand, and is also greatly related to a resolving algorithm on the other hand, and by adopting a proper algorithm, not only can high-frequency errors in Beidou signals be eliminated, but also certain low-frequency long-period errors can be inhibited. Most errors in Beidou monitoring can be eliminated through a double difference algorithm, but a good elimination method for long-period errors such as multipath errors is not available temporarily, and the long-period errors can only be weakened through establishing a model. In some places where shadowing is severe, multipath error settings can reach the centimeter level. Multipath errors are mainly caused by reflections of the satellite signals from buildings or the ground, and therefore exhibit a certain periodicity depending on the operation of the satellite.
The existing effective approaches for mitigating multipath errors are mainly three types: (1) selecting a place with less shielding when data are collected; (2) adding an algorithm for suppressing multipath effect or using an antenna with a choke coil in the GNSS receiver; (3) and eliminating the multipath effect by adopting a post-processing algorithm. The position of a common monitoring point in a project is determined, and an ideal open environment cannot exist, so that the method (1) is not applicable; method (2) requires improvements in hardware, requiring receiver manufacturers to optimize; application in projects will generally employ method (3) to mitigate multipath errors.
The post-processing algorithm is the satellite selection method which is the simplest method, and the method for establishing the multipath model is more common. The two methods firstly calculate the multipath effect of different satellites based on the principle that the multipath effect has large influence on pseudo-range observed values and small influence on carrier observed values; the satellite selection method mainly comprises the steps of selecting a satellite with a small error to participate in resolving according to a multipath error obtained through calculation; the model method establishes a multi-path effect model of each satellite according to multi-day multi-path errors, and then corrects pseudo-range observed values of each satellite according to the model. At present, a multipath modeling method mainly realizes multipath correction through polynomial fitting, Kalman filtering technology, spectrum analysis, signal-to-noise ratio-multipath error and altitude-multipath error.
The existing multipath error weakening method is mainly used in the Beidou data resolving process, and has certain weakening effect but certain limitation. The satellite selection method is simple, but under the condition that the number of observed satellites is small, the method can reduce the resolving precision by removing excessive observed data and can only be used in places with good satellite conditions; for the model method, the pseudo-range observation value of each satellite needs to be modeled and corrected, the visible and invisible conditions of the satellite are troublesome to process, and the satellite is not visible for 24 hours all day, so that the modeling is not very accurate, and in addition, the modeling and the correction need to be carried out in the resolving process, and the processing process is complicated.
Therefore, the method which is suitable for Beidou deformation monitoring and has a good error weakening effect is significant.
Disclosure of Invention
The invention aims to provide a multi-path error weakening method in Beidou deformation monitoring, which is suitable for Beidou deformation monitoring and has a good error weakening effect. The specific scheme is as follows:
a multipath error weakening method in Beidou deformation monitoring comprises the following steps:
step one, obtaining a multipath error: collecting monitoring data of N days at a monitoring point, dividing the monitoring data of the N days into N sections by taking the day as a unit to obtain N sections of monitoring data, and respectively carrying out gross error elimination, low-pass filtering and deformation trend removal on each section of monitoring data to obtain a multi-path error; wherein: n is more than or equal to 1 and less than or equal to 30;
step two, establishing a model: fitting the multipath error by adopting a fitting equation, and selecting a fitting algorithm with the minimum fitting residual error as an optimal fitting algorithm; performing interpolation on the monitoring data according to X hour intervals by using a best fit algorithm, and performing constrained least square estimation on the interpolation data to obtain optimal interpolation data; wherein: the fitting equation is one of unitary quadratic, unitary cubic and unitary quartic equations, and X is more than or equal to 0.1 and less than or equal to 1;
outputting optimal fitting algorithm, minimum fitting residual, optimal interpolation data and time information as model parameters to establish a multi-path error model;
step three, error correction: acquiring multi-path error data from the monitoring data to be corrected of the ith day of the monitoring point by adopting the method for acquiring the multi-path error in the step one; acquiring fitted monitoring data from the multipath error data by adopting an optimal fitting algorithm in the second step; obtaining the fitted monitoring data and the correlation coefficient of the optimal interpolation data in the multipath error model; i, taking a natural number which is more than or equal to 1;
step four, judging the correlation coefficient, if the correlation coefficient is larger than a set value, carrying out model correction, and entering the next step; otherwise, directly outputting the fitted monitoring data;
step five, updating the model: and taking i as i +1, and taking the fitted monitoring data in the third step as a multipath error to return to the second step.
Preferably, in the above technical solution, the removing of the deformation tendency in the first step specifically includes: and respectively carrying out equation fitting once in one unit on the monitoring data of N days to obtain a fitting equation as an expression 1):
y＝ax+b 1)；
wherein: y is the x-th monitoring data after fitting, a is a fitting coefficient, x is the x-th monitoring data after low-pass filtering, and b is a constant term;
removing trend items in the monitoring data according to expression 2) to obtain multipath error data:
y，＝yx-ax-b 2)；
wherein: y is the multipath error data, yxThe x-th monitoring data collected for the monitoring point, x is the x-th monitoring data after low-pass filtering, and a is fittingCoefficient, b is a constant term.
Preferably, in the above technical solution, in the second step: fitting each section of multipath error obtained in the first step when fitting the multipath error by adopting a fitting equation; the fitting algorithm for selecting the minimum fitting residual error specifically comprises the following steps: firstly, acquiring the fitting residual error of each section of multipath error, then comparing the fitting residual errors of each section of multipath error, and selecting the smallest fitting residual error;
the acquisition of the optimal interpolation data specifically comprises the following steps: sequentially interpolating the monitoring data according to the arrangement sequence of the monitoring data of each section at X-hour intervals, and respectively obtaining an interpolation data set after interpolation of the monitoring data of each section; performing constrained least square estimation on the interpolation data sets of all the sections to select optimal interpolation data;
the data volume in the model data set in the multipath error model is at most 30 days; if the data reaches 30 days, processing the data in the data set according to rules of storing new data and discarding old data;
and X is 0.5.
Preferably, in the above technical solution, the set value of the correlation coefficient in step four is 0.5
The technical scheme of the invention has the following beneficial effects:
1. the method comprises five steps of acquiring multipath errors, establishing a model, correcting the errors, judging correlation coefficients and updating the model, and is simple and convenient to operate; acquiring multipath errors, namely removing high-frequency signals in monitoring data and the moving trend of monitoring points, and extracting the multipath errors through curve fitting; the model building step is mainly used for building a multi-path error model for monitoring data of multiple days and outputting model parameters according to a specified format; the error correction step is mainly to compare the fitted monitoring data after the processing of the monitoring data to be corrected with the optimal interpolation data in the multipath error model to obtain a correlation coefficient; and then judging the correlation coefficient and updating the model. The method can directly weaken the influence of multipath errors in the data processing process (namely in the resolving process); the error correction of the invention adopts the monitoring data of one day, the model is updated in time, and the monitoring precision is improved.
2. Compared with the prior art, the multipath error weakening method does not need to know the specific multipath effect condition and the specific calculation algorithm of each Beidou satellite, only needs to directly establish a simple model according to the calculation result, has simple algorithm and can achieve the effect of weakening the error.
3. The optimal fitting algorithm is obtained by calculating monitoring data of N days, preferably multiple days (such as more than or equal to 5 and less than or equal to 10), and has high accuracy; coarse difference elimination, low-pass filtering and deformation trend removal are carried out on each section of monitoring data, high-frequency signals in the monitoring data in a certain period of time and the moving trend of monitoring points can be effectively removed, and multi-path errors are effectively obtained.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart for acquiring multipath errors.
Detailed Description
Embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways, which are defined and covered by the claims.
Example (b):
a multipath error weakening method in Beidou deformation monitoring comprises the following steps:
the method comprises the following steps of firstly, acquiring multipath errors, specifically:
firstly, collecting monitoring data (obtained by calculation software in the existing Beidou positioning system) of monitoring points for 10 days, and taking the monitoring data of the previous 10 days as a model data set; then dividing the 10-day monitoring data into 10 sections by taking the day as a unit to obtain 10 sections of monitoring data; and (3) performing gross error elimination, low-pass filtering and deformation trend removal on each section of monitoring data respectively, thereby removing high-frequency signals and the moving trend of monitoring points in the monitoring data at a certain time period to obtain 10 sections of multipath errors, which are shown in detail in figure 1. The deformation tendency removal specifically comprises the following steps:
respectively carrying out equation fitting once in one unit on the monitoring data of each section to obtain a fitting equation as an expression 1):
y＝ax+b 1)；
wherein: y is the x-th monitoring data after fitting, a is a fitting coefficient, x is the x-th monitoring data after low-pass filtering, and b is a constant term;
removing trend items in the monitoring data according to expression 2) to obtain multipath error data:
y，＝yx-ax-b 2)；
wherein: y' is multipath error data, yxThe method comprises the steps that the x-th monitoring data collected for a monitoring point are obtained, x is the x-th monitoring data after low-pass filtering, a is a fitting coefficient, and b is a constant term.
Step two, establishing a model, specifically:
respectively fitting the multipath errors by adopting a fitting equation (a unitary cubic equation is preferred here) (wherein, the initial model establishment is to fit the 10 multipath errors in the first step, and when the model is updated, the data returned in the fifth step are fitted), and selecting the fitting algorithm with the minimum fitting residual error as the optimal fitting algorithm (specifically, the fitting residual error of each multipath error is obtained firstly, then the fitting residual errors of each multipath error are compared, and the minimum fitting residual error is selected); the optimal fitting algorithm is utilized to interpolate the monitoring data according to 0.5 hour intervals, and the interpolated data is subjected to constrained least square estimation to obtain optimal interpolated data, which specifically comprises the following steps: sequentially interpolating the monitoring data at intervals of 0.5 hour according to the arrangement sequence of each section of monitoring data, and respectively obtaining an interpolation data set after each section of monitoring data is interpolated (taking 10 days as an example, 10 sections of data sets each containing 48 data are obtained here); performing constrained least square estimation on the interpolation data sets of all the sections to select optimal interpolation data;
will be optimalThe fitting algorithm, the minimum fitting residual, the optimal interpolation data and the time information are used as model parameters to output and establish a multi-path error model according to the format of the figure 1, and the method specifically comprises the following steps: writing a fitting Method into a # Method field in a configuration file of table 1, writing a best fitting coefficient into a # Param field in the configuration file of table 1, writing minimum fitting residuals of all Time points into STDX, STDY and STDZ in table 1, and averaging 10 segments of multipath error data obtained in the second step according to the Time point in table 1 to obtain X, Y, Z multipath error model values in three directions, wherein: SiteID in the first row is monitoring point ID; the Method in the second row is the modeling Method adopted, and 1 and 3 represent that a unitary cubic equation fitting algorithm is adopted; param in the third row is the algorithm parameter, and 1.5, 2.4, 1.2, and 5.2 represent the following equations: y 1.5x3+2.4x2+1.2x+5.2。
TABLE 1 model parameters
SiteID 15
Method 1 3
Param 1.5 2.4 1.2 5.2
Time X Y Z STDX STDY STDZ
0.5 2.17 4.22 4.41 0.60 0.55 1.48
1.0 3.47 2.16 9.33 1.86 1.08 0.20
1.5 9.24 8.59 4.50 0.48 0.81 0.05
2.0 9.75 2.26 8.25 1.11 1.74 2.93
2.5 2.74 1.92 3.90 0.24 0.77 1.56
3.0 5.33 6.35 10.42 1.94 1.46 0.44
3.5 2.75 3.80 8.40 0.56 0.40 2.34
4.0 9.82 1.38 10.16 1.19 1.19 1.67
4.5 10.55 10.55 2.41 1.92 0.48 2.53
5.0 9.79 3.40 9.72 1.26 0.37 2.41
5.5 4.21 4.27 10.88 0.96 0.62 2.26
6.0 1.33 9.70 2.55 0.71 0.28 0.27
6.5 8.24 3.56 10.09 0.31 1.26 0.01
7.0 6.18 7.31 9.27 1.05 0.49 0.16
7.5 1.35 10.64 10.78 1.73 0.86 0.95
8.0 5.11 2.21 9.05 1.28 0.47 1.10
8.5 5.55 8.46 7.91 1.95 1.30 0.17
Thirdly, error correction, which specifically comprises the following steps:
acquiring multipath error data from monitoring data to be corrected on the ith day (i is a natural number which is greater than or equal to 1 and generally starts from the first day after a model is established) of a monitoring point by adopting a method for acquiring multipath errors in the first step; fitting monitoring data obtained by the multipath error data by adopting an optimal fitting algorithm in the second step; and acquiring the correlation coefficient of the fitted monitoring data and the optimal interpolation data in the multipath error model (which can be calculated by adopting a conventional method in the prior art).
In this step: the data volume in the model data set in the multipath error model is at most 30 days; if the data reaches 30 days, processing the data in the data set according to rules of storing new data and discarding old data, and automatically discarding the data on the 1 st day if the data on the 31 st day needs to be stored; if the data of the 32 nd day needs to be saved, the data of the 2 nd day is automatically discarded; and so on.
Fourthly, judging the correlation coefficient: if the correlation coefficient is larger than a set value (the set value is 0.5), carrying out model correction, and entering the next step; otherwise, the monitoring data after fitting is directly output without correction.
Step five, updating the model: and (3) taking i as i +1, returning the fitted monitoring data in the third step to the second step for calculation, obtaining the optimal fitting algorithm and the optimal interpolation data again, and outputting the optimal fitting algorithm, the minimum fitting residual error, the optimal interpolation data and the time information as model parameters to establish the multi-path error model again (namely, updating the model is realized).
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (3)

1. A multi-path error weakening method in Beidou deformation monitoring is characterized by comprising the following steps:
step one, obtaining a multipath error: collecting monitoring data of N days at a monitoring point, dividing the monitoring data of the N days into N sections by taking the day as a unit to obtain N sections of monitoring data, and respectively carrying out gross error elimination, low-pass filtering and deformation trend removal on each section of monitoring data to obtain a multi-path error; wherein: n is more than or equal to 1 and less than or equal to 30;
the deformation tendency removal specifically comprises the following steps: and respectively carrying out equation fitting once in one unit on the monitoring data of N days to obtain a fitting equation as an expression 1):
y＝ax+b 1)；
wherein: y is the x-th monitoring data after fitting, a is a fitting coefficient, x is the x-th monitoring data after low-pass filtering, and b is a constant term;
removing trend items in the monitoring data according to expression 2) to obtain multipath error data:
y'＝yx-ax-b 2)；
wherein: y' is multipath error data, yxThe method comprises the steps that x is the x-th monitoring data collected by a monitoring point, x is the x-th monitoring data after low-pass filtering, a is a fitting coefficient, and b is a constant term;
step two, establishing a model: fitting the multipath error by adopting a fitting equation, and selecting a fitting algorithm with the minimum fitting residual error as an optimal fitting algorithm; performing interpolation on the monitoring data according to X hour intervals by using a best fit algorithm, and performing constrained least square estimation on the interpolation data to obtain optimal interpolation data; wherein: the fitting equation is one of unitary quadratic, unitary cubic and unitary quartic equations, and X is more than or equal to 0.1 and less than or equal to 1;
outputting optimal fitting algorithm, minimum fitting residual, optimal interpolation data and time information as model parameters to establish a multi-path error model;
step three, error correction: acquiring multi-path error data from the monitoring data to be corrected of the ith day of the monitoring point by adopting the method for acquiring the multi-path error in the step one; acquiring fitted monitoring data from the multipath error data by adopting an optimal fitting algorithm in the second step; obtaining the fitted monitoring data and the correlation coefficient of the optimal interpolation data in the multipath error model; i, taking a natural number which is more than or equal to 1;
step four, judging the correlation coefficient, if the correlation coefficient is larger than a set value, carrying out model correction, and entering the next step; otherwise, directly outputting the fitted monitoring data;
step five, updating the model: and taking i as i +1, and taking the fitted monitoring data in the third step as a multipath error to return to the second step.
2. The method for attenuating the multipath error in Beidou deformation monitoring according to claim 1, wherein in the second step: fitting each section of multipath error obtained in the first step when fitting the multipath error by adopting a fitting equation; the fitting algorithm for selecting the minimum fitting residual error specifically comprises the following steps: firstly, acquiring the fitting residual error of each section of multipath error, then comparing the fitting residual errors of each section of multipath error, and selecting the smallest fitting residual error;
the acquisition of the optimal interpolation data specifically comprises the following steps: sequentially interpolating the monitoring data according to the arrangement sequence of the monitoring data of each section at X-hour intervals, and respectively obtaining an interpolation data set after interpolation of the monitoring data of each section; performing constrained least square estimation on the interpolation data sets of all the sections to select optimal interpolation data;
the data volume in the model data set in the multipath error model does not exceed 30 days; if the data reaches 30 days, processing the data in the data set according to rules of storing new data and discarding old data;
and X is 0.5.
3. The method for attenuating the multi-path error in Beidou deformation monitoring according to claim 1, wherein the set value of the correlation coefficient in the fourth step is 0.5.
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