CN113466908A - GNSS positioning accuracy enhancing method - Google Patents

GNSS positioning accuracy enhancing method Download PDF

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CN113466908A
CN113466908A CN202110964090.6A CN202110964090A CN113466908A CN 113466908 A CN113466908 A CN 113466908A CN 202110964090 A CN202110964090 A CN 202110964090A CN 113466908 A CN113466908 A CN 113466908A
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gnss
satellite
rover
station
pseudorange
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朱青永
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Shenzhen Dianka Measurement And Control Technology Co ltd
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Shenzhen Dianka Measurement And Control 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
    • G01S19/38—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
    • G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
    • G01S19/423—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between position solutions derived from different satellite radio beacon positioning systems
    • 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
    • G01S19/33—Multimode operation in different systems which transmit time stamped messages, e.g. GPS/GLONASS
    • 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
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • 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
    • G01S19/40—Correcting position, velocity or attitude
    • G01S19/41—Differential correction, e.g. DGPS [differential GPS]

Abstract

The invention discloses a GNSS positioning accuracy enhancing method, which comprises the following steps: step S1, constructing a GNSS positioning function based on the rover station, the reference station and the GNSS satellite system; and step S2, performing mapping approximation on the GNSS positioning function by using a neural network, and converting the GNSS positioning function into a GNSS accurate positioning model to enhance the GNSS positioning accuracy. According to the invention, by utilizing the GNSS combined positioning system fusing the GPS satellite system, the BDS satellite system, the GLONASS satellite system and the Galileo satellite system, more available satellites can be obtained, the space geometric structure of the satellites is improved, the availability, the reliability and the accuracy of the GNSS satellite system are improved, pseudo-range differential positioning is carried out on the GNSS satellite system to obtain a positioning function for the position of a GNSS receiver, the positioning function is subjected to nonlinear mapping by utilizing a neural network to obtain a GNSS accurate positioning model which is completely matched with the mapping relation between satellite positioning data, and the positioning operation efficiency and the accuracy are high.

Description

GNSS positioning accuracy enhancing method
Technical Field
The invention relates to the technical field of satellite positioning, in particular to a GNSS positioning precision enhancing method.
Background
Global Navigation Satellite Systems (GNSS) generally refer to systems that allow a position fix (positionfix) to be determined based on GNSS signals received from a plurality of GNSS satellites. Each GNSS satellite transmits a GNSS signal that identifies the satellite and the time of signal transmission. The GNSS antenna/receiver is configured to receive each of the GNSS signals transmitted by the visible GNSS satellites and determine a pseudorange or range from the GNSS antenna/receiver to the respective GNSS satellite using a time of flight of each GNSS signal and a known position of each GNSS satellite. The plurality of calculated pseudoranges are used to trilaterate a position of the GNSS antenna/receiver in three dimensional space. Types of GNSS systems include Global Positioning System (GPS), GLONASS (GLONASS), Galileo (Galileo), Beidou (BDS), and the like.
Prior art CN202011024660.5 discloses a system and method for position location estimation using two or more antennas, comprising a first GNSS antenna/receiver, a second GNSS antenna/receiver and a GNSS processor system. The first GNSS antenna/receiver is located at a first location and a first pseudorange is computed based on the received GNSS signals. The second GNSS antenna/receiver is located at a second location at a known distance from the first GNSS antenna/receiver, wherein the second GNSS antenna/receiver calculates a second pseudorange based on the received GNSS signals. The GNSS processor system is configured to receive the first pseudorange and the second pseudorange, wherein in response to the GNSS processor system identifying one of the first and second pseudoranges as erroneous and one of the first and second pseudoranges as valid, the GNSS processor system calculates a corrected pseudorange and uses the corrected pseudorange and the valid pseudorange to determine a GNSS position location estimate for the first GNSS antenna/receiver and the second GNSS antenna/receiver, but sequentially screens corrections for a position estimate relying only on pseudorange data, is highly data-adhesive, can cause a position estimate to fail in the event of a data error, and is difficult to completely match a mapping relationship between satellite positioning data for position resolution using a fixed mathematical function, with low overall accuracy and efficiency.
Disclosure of Invention
The invention aims to provide a GNSS positioning accuracy enhancement method, which aims to solve the technical problems that in the prior art, positioning estimation is carried out only by sequentially screening and correcting pseudo-range data, the data adhesiveness is high, once one datum is wrong, the positioning estimation is invalid, the positioning calculation by using a fixed mathematical function is difficult to completely match the mapping relation between satellite positioning data, the overall accuracy is low, and the efficiency is low.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a GNSS positioning accuracy enhancement method comprises the following steps:
step S1, constructing a GNSS positioning function based on the rover station, the reference station and the GNSS satellite system;
and step S2, performing mapping approximation on the GNSS positioning function by using a neural network, and converting the GNSS positioning function into a GNSS accurate positioning model to enhance the GNSS positioning accuracy.
As a preferable aspect of the present invention, in step S1, the method for constructing the GNSS positioning function includes:
step S101, a reference pseudo-range observation equation is constructed based on a reference station and a GNSS satellite system, the reference pseudo-range observation equation is characterized by pseudo-range observation values from the reference station to all satellites in the GNSS satellite system, and the reference pseudo-range observation equation is as follows:
Figure BDA0003223350170000021
where B is characterized as a reference station, j is characterized as a satellite,
Figure BDA0003223350170000022
is a pseudorange observation for the reference station to satellite j,
Figure BDA0003223350170000023
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000024
ionospheric and tropospheric errors of the reference station,
Figure BDA0003223350170000025
for pseudorange observation noise and other unmodeled errors, (X)B,YB,ZB) Is the three-dimensional coordinate of the reference station, (X)j,Yj,Zj) Three-dimensional coordinates of the satellite at the moment of transmitting the signal data for the satellite j;
step S102, a flowing pseudorange observation equation is constructed based on the rover station and the GNSS satellite system, the reference pseudorange observation equation is characterized by pseudorange observation values of all satellites in the GNSS satellite system from the rover station, and the flowing pseudorange observation equation is as follows:
f2＝PM j＝ρM j+δtM j+IM j+TM j+εM j；
where M is characterized as a rover, j is characterized as a satellite,
Figure BDA0003223350170000026
for pseudorange observations from rover to satellite j,
Figure BDA0003223350170000027
geometric distance of rover to satellite j, δ tM jThe rover receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000031
ionospheric and tropospheric errors of the rover,
Figure BDA0003223350170000032
pseudo-range observation noise and other non-modeled errors;
step S103, constructing a GNSS positioning function based on the reference pseudorange observation equation and the flowing pseudorange observation equation, wherein the GNSS positioning function is characterized in a nonlinear mapping relation between the three-dimensional coordinates of the rover station and the pseudorange observation values and the three-dimensional coordinates of all satellites in the GNSS satellite system.
As a preferred embodiment of the present invention, the step S101 further includes correcting the reference pseudorange observation equation to obtain a pseudorange correction equation, where the pseudorange correction equation is characterized by pseudorange correction values from a reference station to all satellites in the GNSS satellite system, where the pseudorange correction equation is:
ΔρB j＝PB j-ρB j＝δtB j+IB j+TB j+εB j；
where B is characterized as a reference station, j is characterized as a satellite,
Figure BDA0003223350170000033
is a pseudorange observation for the reference station to satellite j,
Figure BDA0003223350170000034
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000035
ionospheric and tropospheric errors of the reference station,
Figure BDA0003223350170000036
for pseudorange observation noise and other unmodeled errors, (X)B,YB,ZB) Is the three-dimensional coordinate of the reference station, (X)j,Yj,Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
As a preferable aspect of the present invention, the step S102 further includes:
constructing a pseudo range positioning equation for roughly determining the three-dimensional coordinate of the mobile station based on the pseudo range correction equation and the mobile pseudo range observation equation, wherein the pseudo range positioning equation is as follows:
Figure BDA0003223350170000037
wherein M is characterized as a rover, j tableCharacterized by being a satellite,
Figure BDA0003223350170000038
for pseudorange observations from rover to satellite j,
Figure BDA0003223350170000039
geometric distance of rover to satellite j, δ tM jAs a parameter of the rover receiver's clock difference matrix, deltatB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA00032233501700000310
ionospheric and tropospheric errors of the rover,
Figure BDA00032233501700000311
for pseudorange observation noise and other unmodeled errors, (X)M,YM,ZM) Is the three-dimensional coordinate of the rover (X)j,Yj,Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
As a preferable aspect of the present invention, in step S103, the method for constructing a GNSS positioning function includes:
setting b as δ tM j-δtB j，SV＝(XM,YM,ZM)，U＝(Xj,Yj,Zj)，Pr＝PM j-ΔρB j，Er＝εM jThe pseudo-range is positioned in an equation g1Performing equation substitution to obtain:
Pr＝|SV-U|+b+Er；
converting Pr ═ SV-U | + b + Er into a nonlinear mapping function form, and obtaining a GNSS positioning function as follows:
SV＝F(U,Pr)；
in the formula, F is characterized as a nonlinear mapping function body of a GNSS positioning function, SV is characterized as a three-dimensional coordinate of a rover station, U is characterized as a three-dimensional coordinate of a satellite at the moment when a signal data is transmitted by a satellite j, Pr is characterized as a pseudo-range correlation value of the rover station and a reference station, b is characterized as a clock error correlation value of the rover station and the reference station, and Er is characterized as pseudo-range observation noise and other unmodeled errors.
As a preferred embodiment of the present invention, in step S2, the method for converting the GNSS precise positioning model includes:
step S201, building a neural network, and converting the GNSS positioning function into a neural network model form based on the neural network;
step S202, a training sample is collected, model training is carried out on the neural network based on the training sample to obtain the GNSS accurate positioning model, and accurate output of the three-dimensional coordinates of the mobile station is achieved by means of the pseudo-range observation value and the three-dimensional coordinates of all satellites in the GNSS satellite system.
As a preferable aspect of the present invention, in step S201, the method for building a neural network includes:
setting inputs of the neural network as U and Pr and an output as SV based on a GNSS positioning function SV ═ F (U, Pr), wherein the inputs of the neural network are in the form of:
{Ijt|j∈[1,n],t∈[1,T]}，
wherein the content of the first and second substances,
Figure BDA0003223350170000041
Utthree-dimensional coordinates (X) characterized by the moment t at which the satellite j transmits the signal datajt，Yjt，Zjt)，PrtPseudorange correlation value P for rover station and reference station characterized by time tM jt-ΔρB jtT is represented as a final value of the moment T, and n is the total number of satellites in the GNSS satellite system;
the output form of the neural network is as follows: { Ot|t∈[1,T]}，
Wherein the content of the first and second substances,
Figure BDA0003223350170000051
SVtcharacterised by the three-dimensional coordinates (X) of the rover at the instant tMt，YMt，ZMt) T is characterized as the final value of time T;
the network body of the neural network is set as an ELM neural network, the neuron of a hidden layer is set as 8500, the neuron activation function is set as a sin function, and the loss function is set as a root mean square error function.
As a preferable aspect of the present invention, in step S202, the method for acquiring the training sample includes:
uniformly arranging a flow grid in a target area, and distributing a reference station and a rover station in the flow grid to obtain three-dimensional coordinates SV of the rover station in a time intervaltAnd the three-dimensional coordinate U of the satellite at the moment when the satellite j transmits the signal datatAnd pseudorange correlation Pr of rover and reference stationstAs training samples [ I ] with timing propertiesjt,Ot]。
As a preferred scheme of the invention, the training sample [ Ijt，Ot]And substituting the neural network to carry out model training to obtain the GNSS accurate positioning model.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, by utilizing the GNSS combined positioning system fusing the GPS satellite system, the BDS satellite system, the GLONASS satellite system and the Galileo satellite system, more available satellites can be obtained, the space geometric structure of the satellites is improved, the availability, the reliability and the accuracy of the GNSS satellite system are improved, pseudo-range differential positioning is carried out on the GNSS satellite system to obtain a positioning function for the position of a GNSS receiver, the positioning function is subjected to nonlinear mapping by utilizing a neural network to obtain a GNSS accurate positioning model which is completely matched with the mapping relation between satellite positioning data, and the positioning operation efficiency and the accuracy are high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flowchart of a GNSS positioning accuracy enhancement method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of the distribution structure of the reference station and the rover station in the flow grid according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, GNSS receivers are disposed in both the base station and the rover station, the GNSS receivers in the base station and the rover station simultaneously receive satellite signals of all satellites of the GNSS satellite system formed by satellite signal data of the GPS satellite system, the BDS satellite system, the GLONASS satellite system and the Galileo satellite system, and simultaneously fuse the GPS satellite system, the BDS satellite system, the GLONASS satellite system and the Galileo satellite system, so that more available satellites can be obtained for positioning, and a more bulky satellite space structure is constructed, so that coordinates of a GNSS receiver position obtained in the bulky satellite space structure are more accurate, and a linear resolving function used in positioning resolving of the GNSS satellite system cannot completely match a mapping relationship between positioning data, so that the invention provides a GNSS positioning accuracy enhancing method, which trains a large amount of data by using a neural network to obtain a GNSS accurate positioning model completely matching the mapping relationship between positioning data, and the positioning calculation precision is improved.
The invention provides a GNSS positioning accuracy enhancing method, which comprises the following steps:
step S1, constructing a GNSS positioning function based on the rover station, the reference station and the GNSS satellite system;
in step S1, the method for constructing the GNSS positioning function includes:
step S101, a reference pseudo-range observation equation is constructed based on a reference station and a GNSS satellite system, and the reference pseudo-range observation equation is characterized by pseudo-range observation values of all satellites in the GNSS satellite system from the reference station, wherein the reference pseudo-range observation equation is as follows:
Figure BDA0003223350170000061
where B is characterized as a reference station, j is characterized as a satellite,
Figure BDA0003223350170000062
is a pseudorange observation for the reference station to satellite j,
Figure BDA0003223350170000063
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000071
ionospheric and tropospheric errors of the reference station,
Figure BDA0003223350170000072
for pseudorange observation noise and other unmodeled errors, (X)B,YB,ZB) Is the three-dimensional coordinate of the reference station, (X)j,Yj,Zj) Three-dimensional coordinates of the satellite at the moment of transmitting the signal data for the satellite j;
step S101 further comprises the step of correcting the reference pseudo-range observation equation to obtain a pseudo-range correction equation, wherein the pseudo-range correction equation is characterized by pseudo-range correction values of all satellites in the GNSS satellite system from the reference station, and the pseudo-range correction equation is as follows:
ΔρB j＝PB j-ρB j＝δtB j+IB j+TB j+εB j；
where B is characterized as a reference station, j is characterized as a satellite,
Figure BDA0003223350170000073
is a pseudorange observation for the reference station to satellite j,
Figure BDA0003223350170000074
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000075
ionospheric and tropospheric errors of the reference station,
Figure BDA00032233501700000712
for pseudorange observation noise and other unmodeled errors, (X)B,YB,ZB) Is the three-dimensional coordinate of the reference station, (X)j,Yj,Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
Step S102, a flowing pseudorange observation equation is constructed based on the rover station and the GNSS satellite system, the reference pseudorange observation equation is characterized by pseudorange observation values of all satellites in the rover station and the GNSS satellite system, and the flowing pseudorange observation equation is as follows:
f2＝PM j＝ρM j+δtM j+IM j+TM j+εM j；
where M is characterized as a rover, j is characterized as a satellite,
Figure BDA0003223350170000076
for pseudorange observations from rover to satellite j,
Figure BDA0003223350170000077
geometric distance of rover to satellite j, δ tM jThe rover receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000078
ionospheric and tropospheric errors of the rover,
Figure BDA0003223350170000079
pseudo-range observation noise and other non-modeled errors;
step S102 further includes:
the ionosphere error and the troposphere error of the reference station and the rover station have spatial correlation, and the troposphere delay and the ionosphere delay of the reference station and the rover station are kept equal in the process of constructing the first positioning equation, namely
Figure BDA00032233501700000710
Constructing a pseudo-range positioning equation for roughly determining the three-dimensional coordinate of the mobile station based on the pseudo-range correction equation and the mobile pseudo-range observation equation, wherein the pseudo-range positioning equation is as follows:
Figure BDA00032233501700000711
where M is characterized as a rover, j is characterized as a satellite,
Figure BDA0003223350170000081
for pseudorange observations from rover to satellite j,
Figure BDA0003223350170000082
geometric distance of rover to satellite j, δ tM jAs a parameter of the rover receiver's clock difference matrix, deltatB jAs the reference station receiver and satellite clock error matrix parameters,
Figure BDA0003223350170000083
ionospheric and tropospheric errors of the rover,
Figure BDA0003223350170000084
for pseudorange observation noise and other unmodeled errors, (X)M,YM,ZM) Is the three-dimensional coordinate of the rover (X)j,Yj,Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
The steps effectively quantize the intersystem deviation, can effectively weaken common errors, increase observed quantity and improve the strength and performance of parameter solution; the GNSS positioning function is constructed through the system difference principle, common errors such as satellite clock error, receiver clock error of a reference station and a rover station, model deviation and the like can be eliminated, and finally the model strength and the parameter estimation performance of parameter solution are improved to achieve accurate positioning.
Step S103, constructing a GNSS positioning function based on the reference pseudo-range observation equation and the flowing pseudo-range observation equation, wherein the GNSS positioning function is represented by a nonlinear mapping relation between the three-dimensional coordinates of the rover station and the pseudo-range observation values and the three-dimensional coordinates of all satellites in the GNSS satellite system.
In step S103, the method for constructing the GNSS positioning function includes:
setting b as δ tM j-δtB j，SV＝(Xm,Ym,Zm)，U＝(Xj,Yj,Zj)，Pr＝PM j-ΔρB j，Er＝εM jPosition the pseudorange to an equation g1Performing equation substitution to obtain:
Pr＝|SV-U|+b+Er；
converting Pr ═ SV-U | + b + Er into a nonlinear mapping function form, and obtaining a GNSS positioning function as follows:
SV＝F(U,Pr)；
in the formula, F is characterized as a nonlinear mapping function body of a GNSS positioning function, SV is characterized as a three-dimensional coordinate of a rover station, U is characterized as a three-dimensional coordinate of a satellite at the moment when a signal data is transmitted by a satellite j, Pr is characterized as a pseudo-range correlation value of the rover station and a reference station, b is characterized as a clock error correlation value of the rover station and the reference station, and Er is characterized as pseudo-range observation noise and other unmodeled errors.
Pr ═ SV-U | + b + Er is a continuous and differentiable function, there is no closed form solution, the neural network can infinitely approximate to a continuous differentiable function under the condition of sufficient training samples, so that a nonlinear mapping function form SV (U, Pr) which converts Pr-SV-U + b + Er function into a mapping relation between infinite approximation positioning data can be utilized, namely, the data relation between SV, U and Pr can be quantized into a nonlinear mapping which is mostly input and output and has a certain fixed form, and F is characterized as a non-linear mapping function body of the GNSS positioning function, and has no specific mathematical expression, however, the method is continuous and slightly applicable, and positioning calculation is performed based on SV ═ F (U, Pr) by using a neural network, so that higher positioning calculation accuracy can be obtained than the method for solving Pr ═ SV-U | + b + Er.
And step S2, performing mapping approximation on the GNSS positioning function by using the neural network, and converting the GNSS positioning function into a GNSS accurate positioning model to enhance the GNSS positioning accuracy.
In step S2, the method for converting the GNSS precise positioning model includes:
step S201, building a neural network, and converting a GNSS positioning function into a neural network model form based on the neural network;
step S202, training samples are collected, model training is carried out on the neural network based on the training samples to obtain a GNSS accurate positioning model, and accurate output of three-dimensional coordinates of the mobile station is achieved by utilizing pseudo-range observation values and three-dimensional coordinates of all satellites in a GNSS satellite system.
In step S201, the method for building a neural network includes:
setting the input of the neural network as U and Pr and the output as SV based on the GNSS positioning function SV ═ F (U, Pr), wherein the input form of the neural network is as follows:
{Ijt|j∈[1,n],t∈[1,T]}，
wherein the content of the first and second substances,
Figure BDA0003223350170000091
Utthree-dimensional coordinates (X) characterized by the moment t at which the satellite j transmits the signal datajt，Yjt，Zjt)，PrtPseudorange correlation value P for rover station and reference station characterized by time tM jt-ΔρB jtT is characterized by the final value of time T, n is GNSS, the total number of satellites in the satellite system;
the output form of the neural network is: { Ot|t∈[1，T]}，
Wherein the content of the first and second substances,
Figure BDA0003223350170000092
SVtcharacterised by the three-dimensional coordinates (X) of the rover at the instant tMt,YMt,ZMt) T is characterized as the final value of time T;
the network body of the neural network is set as an ELM neural network, the hidden layer neuron is set as 8500, the neuron activation function is set as a sin function, and the loss function is set as a root mean square error function.
The ELM neural network used in this embodiment may also use a BP neural network, an LS network, and the like, all of which are within the protection scope of this embodiment.
Based on the principle of the neural network, as long as the neural network with enough hidden layer nodes can approach the nonlinear mapping F infinitely, the method of the neural network is utilized, the neural network is trained to approach the nonlinear mapping F, the method of solving the equation Pr (SV-U + b + Er) is replaced to obtain the three-dimensional coordinate of the rover station, and only when positioning and resolving are carried out, the observed value is input to a GNSS accurate positioning model trained by the neural network, and the three-dimensional coordinate of the rover station can be obtained.
As shown in fig. 2, in step S202, the method for acquiring training samples includes:
uniformly arranging a flow grid in a target area, and distributing a reference station and a rover station in the flow grid to obtain three-dimensional coordinates SV of the rover station in a time intervaltAnd the three-dimensional coordinate U of the satellite at the moment when the satellite j transmits the signal datatAnd pseudorange correlation Pr of rover and reference stationstAs training samples [ I ] with timing propertiesjt,Ot]。
Will train sample [ Ijt,Ot]And substituting the model into a neural network to carry out model training to obtain the GNSS accurate positioning model.
In this embodiment, the number of the reference stations and the rover stations in the rover network is not limited, and can be customized by a user, and when the number of the reference stations and the rover stations is increased, the accuracy of the GNSS precise positioning model is improved, but the requirement of the structural scale of the neural network is higher, so that the accuracy and efficiency need to be balanced when selecting.
According to the invention, by utilizing the GNSS combined positioning system fusing the GPS satellite system, the BDS satellite system, the GLONASS satellite system and the Galileo satellite system, more available satellites can be obtained, the space geometric structure of the satellites is improved, the availability, the reliability and the accuracy of the GNSS satellite system are improved, pseudo-range differential positioning is carried out on the GNSS satellite system to obtain a positioning function for the position of a GNSS receiver, the positioning function is subjected to nonlinear mapping by utilizing a neural network to obtain a GNSS accurate positioning model which is completely matched with the mapping relation between satellite positioning data, and the positioning operation efficiency and the accuracy are high.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.

Claims (9)

1. A GNSS positioning accuracy enhancing method is characterized in that: the method comprises the following steps:
step S1, constructing a GNSS positioning function based on the rover station, the reference station and the GNSS satellite system;
and step S2, performing mapping approximation on the GNSS positioning function by using a neural network, and converting the GNSS positioning function into a GNSS accurate positioning model to enhance the GNSS positioning accuracy.
2. The GNSS positioning accuracy enhancing method according to claim 1, wherein: in step S1, the method for constructing the GNSS positioning function includes:
step S101, a reference pseudo-range observation equation is constructed based on a reference station and a GNSS satellite system, the reference pseudo-range observation equation is characterized by pseudo-range observation values from the reference station to all satellites in the GNSS satellite system, and the reference pseudo-range observation equation is as follows:
Figure FDA0003223350160000011
where B is characterized as a reference station, j is characterized as a satellite,
Figure FDA0003223350160000012
is a pseudorange observation for the reference station to satellite j,
Figure FDA0003223350160000013
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure FDA0003223350160000014
ionospheric and tropospheric errors of the reference station,
Figure FDA0003223350160000015
for pseudorange observation noise and other unmodeled errors, (X)B，YB，ZB) Is the three-dimensional coordinate of the reference station, (X)j，Yj，Zj) Three-dimensional coordinates of the satellite at the moment of transmitting the signal data for the satellite j;
step S102, a flowing pseudorange observation equation is constructed based on the rover station and the GNSS satellite system, the reference pseudorange observation equation is characterized by pseudorange observation values of all satellites in the GNSS satellite system from the rover station, and the flowing pseudorange observation equation is as follows:
f2＝PM j＝ρM j+δtM j+IM j+TM j+εM j；
where M is characterized as a rover, j is characterized as a satellite,
Figure FDA0003223350160000016
for pseudorange observations from rover to satellite j,
Figure FDA0003223350160000017
geometric distance of rover to satellite j, δ tM jThe rover receiver and satellite clock error matrix parameters,
Figure FDA0003223350160000018
ionospheric and tropospheric errors of the rover,
Figure FDA0003223350160000019
pseudo-range observation noise and other non-modeled errors;
step S103, constructing a GNSS positioning function based on the reference pseudorange observation equation and the flowing pseudorange observation equation, wherein the GNSS positioning function is characterized in a nonlinear mapping relation between the three-dimensional coordinates of the rover station and the pseudorange observation values and the three-dimensional coordinates of all satellites in the GNSS satellite system.
3. The GNSS positioning accuracy enhancing method according to claim 2, wherein: the step S101 further includes correcting the reference pseudorange observation equation to obtain a pseudorange correction equation, where the pseudorange correction equation is characterized by pseudorange correction values from a reference station to all satellites in the GNSS satellite system, where the pseudorange correction equation is:
ΔρB j＝PB j-ρB j＝δtB j+IB j+TB j+εB j；
where B is characterized as a reference station, j is characterized as a satellite,
Figure FDA0003223350160000021
is a pseudorange observation for the reference station to satellite j,
Figure FDA0003223350160000022
geometric distance of the reference station to satellite j, δ tB jAs the reference station receiver and satellite clock error matrix parameters,
Figure FDA0003223350160000023
ionospheric and tropospheric errors of the reference station,
Figure FDA0003223350160000024
for pseudorange observation noise and other unmodeled errors, (X)B，YB，ZB) Is the three-dimensional coordinate of the reference station, (X)j，Yj，Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
4. The GNSS positioning accuracy enhancing method according to claim 3, wherein: the step S102 further includes:
constructing a pseudo range positioning equation for roughly determining the three-dimensional coordinate of the mobile station based on the pseudo range correction equation and the mobile pseudo range observation equation, wherein the pseudo range positioning equation is as follows:
Figure FDA0003223350160000025
where M is characterized as a rover, j is characterized as a satellite,
Figure FDA0003223350160000026
for pseudorange observations from rover to satellite j,
Figure FDA0003223350160000027
geometric distance of rover to satellite j, δ tM jAs a parameter of the rover receiver's clock difference matrix, deltatB jAs the reference station receiver and satellite clock error matrix parameters,
Figure FDA0003223350160000028
ionospheric and tropospheric errors of the rover,
Figure FDA0003223350160000029
for pseudorange observation noise and other unmodeled errors, (X)M，YM，ZM) Is the three-dimensional coordinate of the rover (X)j，Yj，Zj) The three-dimensional coordinates of the satellite at the time of transmitting the signal data for satellite j.
5. The GNSS positioning accuracy enhancing method according to claim 4, wherein: in step S103, the method for constructing the GNSS positioning function includes:
setting b as δ tM j-δtB j，SV＝(XM，YM，ZM)，U＝(Xj，Yj，Zj)，Pr＝PM j-ΔρB j，Er＝εM jThe pseudo-range is positioned in an equation g1Performing equation substitution to obtain:
Pr＝|SV-U|+b+Er；
converting Pr ═ SV-U | + b + Er into a nonlinear mapping function form, and obtaining a GNSS positioning function as follows:
SV＝F(U，Pr)；
in the formula, F is characterized as a nonlinear mapping function body of a GNSS positioning function, SV is characterized as a three-dimensional coordinate of a rover station, U is characterized as a three-dimensional coordinate of a satellite at the moment when a signal data is transmitted by a satellite j, Pr is characterized as a pseudo-range correlation value of the rover station and a reference station, b is characterized as a clock error correlation value of the rover station and the reference station, and Er is characterized as pseudo-range observation noise and other unmodeled errors.
6. The GNSS positioning accuracy enhancing method according to claim 5, wherein: in step S2, the method for converting the GNSS precise positioning model includes:
step S201, building a neural network, and converting the GNSS positioning function into a neural network model form based on the neural network;
step S202, a training sample is collected, model training is carried out on the neural network based on the training sample to obtain the GNSS accurate positioning model, and accurate output of the three-dimensional coordinates of the mobile station is achieved by means of the pseudo-range observation value and the three-dimensional coordinates of all satellites in the GNSS satellite system.
7. The GNSS positioning accuracy enhancing method according to claim 6, wherein in the step S201, the method for building the neural network includes:
setting inputs of the neural network as U and Pr and an output as SV based on a GNSS positioning function SV ═ F (U, Pr), wherein the inputs of the neural network are in the form of:
{Ijt|j∈[1，n]，t∈[1，T]}，
wherein the content of the first and second substances,
Figure FDA0003223350160000031
Utthree-dimensional coordinates (X) characterized by the moment t at which the satellite j transmits the signal datajt，Yjt，Zjt)，PrtPseudorange correlation value P for rover station and reference station characterized by time tM jt-ΔρB jtT is represented as a final value of the moment T, and n is the total number of satellites in the GNSS satellite system;
the output form of the neural network is as follows: { Ot|t∈[1，T]}，
Wherein the content of the first and second substances,
Figure FDA0003223350160000032
SVtcharacterised by the three-dimensional coordinates (X) of the rover at the instant tMt，YMt，ZMt) T is characterized as the final value of time T;
the network body of the neural network is set as an ELM neural network, the neuron of a hidden layer is set as 8500, the neuron activation function is set as a sin function, and the loss function is set as a root mean square error function.
8. The GNSS positioning accuracy enhancing method according to claim 7, wherein in the step S202, the method for acquiring the training samples includes:
uniformly arranging a flow grid in a target area, and distributing a reference station and a rover station in the flow grid to obtain three-dimensional coordinates SV of the rover station in a time intervaltAnd the three-dimensional coordinate U of the satellite at the moment when the satellite j transmits the signal datatAnd pseudorange correlation Pr of rover and reference stationstAs training samples [ I ] with timing propertiesjt，Ot]。
9. The GNSS positioning accuracy enhancing method of claim 8, wherein the training samples [ I ]jt，Ot]And substituting the neural network to carry out model training to obtain the GNSS accurate positioning model.
CN202110964090.6A 2021-08-21 2021-08-21 GNSS positioning accuracy enhancing method Pending CN113466908A (en)

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