CN117111112A - GNSS and LNSS data fusion processing method and system - Google Patents
GNSS and LNSS data fusion processing method and system Download PDFInfo
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- 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
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- 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
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Abstract
The invention discloses a GNSS and LNSS data fusion processing method and system, and relates to the technical field of data fusion; the method comprises the following steps: constructing BDS-3 and LNSS inter-satellite ranging observation models, a ground monitoring station GNSS and LNSS observation model and an LEO satellite-borne GNSS observation model according to the acquired observation data; then, respectively determining the observation parameters of each observation model; constructing a GNSS and LNSS multisource data fusion observation model according to multisource parameters by adopting a quadratic polynomial modeling and Lagrangian polynomial interpolation method; performing parameter estimation on the GNSS and LNSS multisource data fusion observation model by using a least square method to obtain a parameter calculation result; the parameter calculation result is used for representing positioning navigation and time service of a satellite system; the invention can provide high-precision and reliable navigation positioning.
Description
Technical Field
The invention relates to the technical field of data fusion, in particular to a GNSS and LNSS data fusion processing method and system.
Background
Global Navigation Satellite Systems (GNSS) are widely used in various fields due to their advantages of all weather, high real time, high accuracy, etc. Currently, there are four GNSS services, namely the Global Positioning System (GPS), the russian GLONASS system (GLONASS), the european union Galileo system (Galileo), and the chinese beidou satellite navigation system (BDS). BDS is independently constructed and operated by china and consists of three satellites of geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium circular earth orbit (MEO). The overall construction is divided into three stages, namely a verification system (BDS-1), an extended regional navigation system (BDS-2) and a global navigation system (BDS-3). BDS-3 formally serves global subscribers, marking that BDS construction is complete with a three-step strategy.
With the increasing demand of users for navigation positioning, elements such as high precision, high reliability, fast convergence and the like are receiving more and more attention from GNSS users, which has prompted the development of Low Earth Orbit (LEO) navigation satellite systems. The lower orbital altitude of LEO satellites compared to GNSS satellites results in the following advantages for LEO navigation satellite systems, i.e., low-orbit navigation satellite systems (LNSS): 1) The change of the geometric structure to the ground is quick, and the positioning convergence time of a user is obviously improved; 2) The signal landing power is high, so that the navigation positioning anti-interference capability is improved; 3) The satellite-ground communication capability is strong, and the GNSS service capability is effectively enhanced. In addition, for BDS, the dense LEO satellite network can be used as a mobile monitoring station, so that the problem that the ground monitoring stations which are independently controllable in China cannot be distributed globally is effectively solved, and the BDS satellite orbit and clock error resolving precision based on the regional ground monitoring stations is remarkably improved.
The multi-navigation system simultaneously promotes the requirements of fusion processing of various observation data, and along with the development of LNSS, signal sources such as LNSS observation of a ground monitoring station, LEO satellite-borne GNSS observation, LEO satellite ISL observation and the like are newly added by the addition of the LNSS on the basis of the GNSS observation of the original ground monitoring station and BDS-3 inter-satellite link (ISL) observation. How to establish a set of multi-source data fusion processing method based on GNSS and LNSS multi-source observation data is important to ensure that the LNSS being constructed provides positioning, navigation and timing (PNT) services with high precision, high reliability and quick convergence.
Disclosure of Invention
The invention aims to provide a GNSS and LNSS data fusion processing method and system, so as to provide high-precision and reliable navigation positioning.
In order to achieve the above object, the present invention provides the following solutions:
a method for processing GNSS and LNSS data fusion, the method comprising:
obtaining observation data; the observation data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data includes: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observation data of LEO satellites;
according to the inter-satellite ranging observation data, constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method;
based on a Lagrangian interpolation algorithm and a quadratic polynomial method, determining observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model; the observed parameters include: signal emission time estimation value, satellite dynamics parameter and satellite clock error;
constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations;
based on a Lagrange interpolation algorithm and an orbit integral function, determining observation parameters of a GNSS and LNSS observation model of a ground monitoring station;
determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining an LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation;
determining the observation parameters of the LEO satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;
constructing a GNSS and LNSS multisource data fusion observation model according to multisource parameters by adopting a quadratic polynomial modeling and Lagrangian polynomial interpolation method; the multi-source parameters include: BDS-3 and LNSS inter-satellite ranging observation model observation parameters, ground monitoring station GNSS and LNSS observation model observation parameters, and LEO satellite on-satellite GNSS observation model observation parameters;
performing parameter estimation on the GNSS and LNSS multi-source data fusion observation model by adopting a least square method to obtain a parameter calculation result; and the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
Optionally, constructing a BDS-3 and LNSS inter-satellite ranging observation model based on a dynamic orbit determination method according to the inter-satellite ranging observation data, which specifically comprises the following steps:
establishing an inter-satellite link ranging observation equation according to the inter-satellite ranging observation data;
establishing a satellite photographed motion equation according to the stress condition in the satellite operation process;
based on a dynamic orbit determination method, determining an inter-satellite ranging observation equation by adopting a Lagrangian interpolation algorithm according to the inter-satellite link ranging observation equation and the satellite photographed motion equation;
and determining a BDS-3 and LNSS inter-satellite ranging observation model according to the inter-satellite ranging observation equation.
Optionally, the expression of the inter-satellite link ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; r is (r) i A position vector of the satellite i in a coordinate system; r is (r) j A position vector of the satellite j in a coordinate system; t is t i The moment of transmitting the signal for satellite i; t is t j The time of receiving the signal for satellite j; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; dT (dT) j For satellite jSatellite clock difference;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
Optionally, the expression of the inter-satellite ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; (X) i ,Y i ,Z i ) Three-dimensional coordinates for satellite i; (X) j ,Y j ,Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
Optionally, the expression of the pseudo-range observation equation is:
the expression of the carrier phase observation equation is:
wherein i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; c is the speed of light; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is a tropospheric delay; d, d ISB Is ISB; ds PCC Is a satellite terminal antenna PCC; dr PCC An antenna PCC for a ground monitoring station; d, d r DCB for the receiver; d, d s DCB for satellite end; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity;an initial phase at the receiver end; />The initial phase of the satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; epsilon φ For each remaining error of the carrier phase observations and observation noise.
Optionally, the expression of the GNSS and LNSS multisource data fusion observation model is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; ρ ij Is the theoretical distance between satellite i and satellite j; c is the speed of light; dT (dT) j Satellite clock difference for satellite j; t is t j The time of receiving the signal for satellite j; dT (dT) i Satellite clock difference for satellite i; t is t i The moment of transmitting the signal for satellite i;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Error in the propagation of the signal from satellite i to satellite j; i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is a tropospheric delay; d, d r DCB for the receiver; d, d s DCB for satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity; />An initial phase at the receiver end; />The initial phase of the satellite end; epsilon φ Each remaining error and observation noise for the carrier phase observations; (X) i ,Y i ,Z i ) Three-dimensional coordinates for satellite i; (X) j ,Y j ,Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j; t is t k Is the kth time; k is a serial number; n is the order of the Lagrangian interpolation algorithm; l is a sequence number; t is t l Is the first moment.
A GNSS and LNSS data fusion processing system, the system comprising:
the data acquisition module is used for acquiring observation data; the observation data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data includes: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observation data of LEO satellites;
the first model construction module is used for constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method according to the inter-satellite ranging observation data;
the first determining module is used for determining the observation parameters of the BDS-3 and LNSS inter-satellite ranging observation model based on a Lagrange interpolation algorithm and a quadratic polynomial method; the observed parameters include: signal emission time estimation value, satellite dynamics parameter and satellite clock error;
the second construction module is used for constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations;
the second determining module is used for determining the observation parameters of the GNSS and LNSS observation models of the ground monitoring station based on the Lagrange interpolation algorithm and the track integral function;
the third construction module is used for determining a satellite-borne GNSS observation equation according to the satellite-borne observation data and determining an LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation;
the third determining module is used for determining the observation parameters of the LEO satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;
the model construction module is used for constructing a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by adopting a method of quadratic polynomial modeling and Lagrangian polynomial interpolation; the multi-source parameters include: BDS-3 and LNSS inter-satellite ranging observation model observation parameters, ground monitoring station GNSS and LNSS observation model observation parameters, and LEO satellite on-satellite GNSS observation model observation parameters;
the solving module is used for carrying out parameter estimation on the GNSS and LNSS multisource data fusion observation model by adopting a least square method to obtain a parameter resolving result; and the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
An electronic device comprising a memory and a processor, the memory configured to store a computer program, the processor configured to execute the computer program to cause the electronic device to perform the GNSS and LNSS data fusion processing method described above.
A computer readable storage medium storing a computer program which when executed by a processor implements the above-described GNSS and LNSS data fusion processing method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a GNSS and LNSS data fusion processing method and system, which construct BDS-3 and LNSS inter-satellite ranging observation models, ground monitoring station GNSS and LNSS observation models and LEO satellite-borne GNSS observation models according to acquired observation data; then, respectively determining the observation parameters of each observation model; constructing a GNSS and LNSS multisource data fusion observation model according to multisource parameters by adopting a quadratic polynomial modeling and Lagrangian polynomial interpolation method; performing parameter estimation on the GNSS and LNSS multisource data fusion observation model by using a least square method to obtain a parameter calculation result; the parameter calculation result is used for representing positioning navigation and time service of a satellite system; the invention can provide high-precision and reliable navigation positioning.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a GNSS and LNSS data fusion processing method according to an embodiment of the present invention;
FIG. 2 is a technical flow chart of a GNSS and LNSS data fusion processing method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a GNSS and LNSS data fusion processing system according to an embodiment of the present invention.
Symbol description:
the system comprises a data acquisition module-1, a first model construction module-2, a first determination module-3, a second construction module-4, a second determination module-5, a third construction module-6, a third determination module-7, a model construction module-8 and a solving module-9.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a GNSS and LNSS data fusion processing method and system, so as to provide high-precision and reliable navigation positioning.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides a method for processing GNSS and LNSS data fusion, where the method includes:
step 100: obtaining observation data; the observed data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observations include: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; ground monitoring station observation data includes: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observations of LEO satellites.
Step 200: and constructing BDS-3 and LNSS inter-satellite distance measurement observation models based on a dynamic orbit determination method according to the inter-satellite distance measurement observation data.
According to inter-satellite ranging observation data, constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method, wherein the method specifically comprises the following steps:
establishing an inter-satellite link ranging observation equation according to the inter-satellite ranging observation data; establishing a satellite photographed motion equation according to the stress condition in the satellite operation process; based on a dynamic orbit determination method, an inter-satellite ranging observation equation is determined by adopting a Lagrange interpolation algorithm according to the inter-satellite link ranging observation equation and a satellite photographed motion equation.
Specifically, the expression of the inter-satellite link ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; r is (r) i A position vector of the satellite i in a coordinate system; r is (r) i A position vector of the satellite j in a coordinate system; t is t i The moment of transmitting the signal for satellite i; t is t j The time of receiving the signal for satellite j; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; dT (dT) j Satellite clock difference for satellite j;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
The expression of the inter-satellite ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; (X) i ,Y i ,Z i ) Three-dimensional coordinates for satellite i; (X) j ,Y j ,Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
And determining the BDS-3 and LNSS inter-satellite ranging observation model according to the inter-satellite ranging observation equation.
Step 300: based on a Lagrangian interpolation algorithm and a quadratic polynomial method, determining observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model; the observed parameters include: signal transmit time estimates, satellite dynamics parameters, and satellite clock differences.
In practical applications, in general, the inter-satellite link ranging observation equation between two satellites can be expressed as:
at present, a dynamic orbit determination method is still a main stream method for satellite orbit determination, and the basic idea is to establish a satellite induced motion equation according to the stress condition in the satellite operation process, and then solve satellite dynamic parameters through an observation equation established by observation data.
When the signal transmitting time of the satellite i and the signal receiving time of the satellite j are node time points of numerical integration, the positions of the satellite i and the satellite j at each numerical integration node are represented by dynamic parameters of the satellite in an initial epoch; in inter-satellite link observations, the observations used for satellite orbit determination come from inter-satellite ranging data. Based on the dynamic orbit determination method, the positions of the satellite i and the satellite j at each numerical integration node can be represented by dynamic parameters of the satellite at an initial epoch, namely:
wherein (X) i ,Y i ,Z i ) And (X) j ,Y j ,Z j ) Representing the three-dimensional coordinates, t, of satellite i and satellite j, respectively k At the kth time, where each node point is represented as a numerical integral, F X 、F Y 、F Z The orbit integral functions respectively representing three coordinate directions of the satellite i, G X 、G Y 、G Z Orbit integral function M respectively representing three coordinate directions of satellite j i And M j The satellite dynamics parameters of the initial epoch for satellite i and satellite j are represented, respectively.
When the signal transmission time of the satellite i and the signal reception time of the satellite j are not node times of numerical integration, the positions of the satellite i and the satellite j at the general time are expressed.
For ranging observation of an inter-satellite link, the signal transmitting time of the satellite i and the signal receiving time of the satellite j may not be node time of numerical integration, and considering that the satellite orbit is smoother, for satellite positions at any non-numerical integration node time, satellite positions of a plurality of numerical integration nodes near the time can be obtained by interpolation based on an n-order Lagrange interpolation method, and according to a Lagrange interpolation formula, the positions of the satellite i and the satellite j at general time can be respectively expressed as:
wherein t is l The first time is here the node time of the numerical integration.
Equation (4) and equation (5) are referred to equation (2) and equation (3) in the calculation process, and after equations (4) and (5) are substituted into equation (1), the inter-satellite ranging observation equation can be expressed as:
as can be seen from equation (6), in the processing methods of various parameters and errors of the inter-satellite non-combination ranging observation model, the method for estimating the time delay parameters of signal transmission and reception, the method for correcting the errors of the phase center and relativity effects of the satellite antenna, and the processing method in the inter-satellite combination ranging observation model are the same, so that the key technology for establishing the inter-satellite non-combination ranging observation model is how to estimate the signal transmission time, the satellite orbit error and the satellite clock error.
Based on an inter-satellite link ranging observation equation (formula 6), the satellite initial moment dynamic parameters, the quadratic term model parameters of each arc segment and the satellite clock error parameters of each node are solved by considering the signal transmitting moment, the satellite orbit error and the satellite clock error.
As for the estimation algorithm of the signal transmitting moment of the inter-satellite link observation, the estimation algorithm of the signal transmitting moment in the GNSS observation can be imitated. The moment of transmission of the signal in the inter-satellite link observation can be expressed as:
wherein Δt is ij Representing the propagation delay of the signal from satellite i to satellite j.
In equation (7), the satellite clock error, the signal transmission delay and the signal receiving delay can be represented by the result of the last parameter estimation, if the parameter estimation is the first parameter estimation cycle, the satellite clock error is obtained from the broadcast ephemeris, and the signal transmission delay and the signal receiving delay can be substituted into the existing prior value due to being stable. For the initial value of signal propagation delay, the initial value can be represented by an inter-satellite ranging observation value, namely:
Δt ij ＝L ij /c (8)
substituting formula (8) into formula (7) can obtain the transmitting moment of the signal, and then calculating the value of the propagation delay of another signal through the track integral function, namely:
if |Deltat ij -Δt ij |>10 -9 Then deltat will be ij And substituting the new signal propagation delay estimated value into the formula (7). The continuous iteration of the formulas (7) and (9) is carried out until the signal transmitting time t is obtained after the requirements are met i Is used for the estimation of the estimated value of (a).
Based on the theoretical distance ρ between satellites after the moment of transmission of the signal obtained by iterative calculation ij Can be expressed as:
and (3) linearizing to obtain:
wherein: dX i 、dY i 、dZ i And dX j 、dY j 、dZ j The partial differential of the satellite i and the satellite j in three coordinate directions is respectively represented, and similarly, for the partial differential of the satellite i and the satellite j in any non-numerical integration node moment in three coordinate directions, the partial differential of the satellite i and the satellite j in general moment in three coordinate directions can be respectively represented as:
wherein f' X 、F' Y 、F' Z An orbit integral partial differential function representing three coordinate directions of the satellite i, G' X 、G' Y 、G' Z An orbit integral partial derivative function representing three coordinate directions of the satellite j,and->Representing initial values of the dynamic parameters at the initial moments of the satellite i and the satellite j respectively. Substituting the formulas (4) and (5) and the formulas (12) and (13) into the formula (11) to obtain a linearized observation equation, and estimating the satellite initial moment dynamic parameters on the linearized observation equation to obtain a satellite orbit determination result.
This results in a time division multiple access at t, since the current inter-satellite link uses time division multiple access k The signal transmission and reception times observed by the different inter-satellite links around the time instant are basically different, and for a satellite, if the satellite clock difference is estimated at each signal reception and transmission time instant, an excessive number of parameters will result. The method adopts the following two methods to process the satellite clock difference in the inter-satellite non-combination ranging observation model.
Regarding the quadratic polynomial method:
the satellite clock differences among different epochs of the same satellite are represented by the same model, but the satellite clock differences are considered to be poor in stability, so that the whole observation arc section cannot be represented by one model like a satellite orbit, the whole observation arc section is required to be divided into a plurality of sections, and the satellite clock differences of all the arc sections are represented by different models. In the method, a quadratic term model is adopted as a basic model, and for the precision of the current satellite clock, the quadratic term model can effectively simulate the satellite clock difference change in a certain arc section length. For satellite i, t is within an arc i The satellite clock difference at time can be expressed as:
dT i (t i )＝A i (t i -t k ) 2 +B i (t i -t k )+C i (14)
wherein A is i 、B i 、C i Respectively representing parameters to be solved of the quadratic term model, t k Indicating the starting moment of the arc segment. After the formula (14) is substituted into the formula (1), modeling of the clock error parameters is realized, namely, the original clock error parameters in the formula (1) are represented by a model, and then solving is to solve the parameters of the model, namely, solving the quadratic term model parameters of each arc section through observation data. For the selection strategy of the arc length, the method provided by the embodiment adopts integral multiple of 3s, and the specific arc length is obtained by comparing the relation among the measured data quantity, the parameter quantity to be estimated and the data processing precision.
Regarding lagrangian interpolation:
considering that the change of the satellite clock difference is stable, the satellite clock difference of a plurality of nodes with equal intervals (such as intervals of 30 s) in the observation arc section can be selected as a resolving parameter, and the satellite clock difference of the rest epochs can be obtained by interpolating the satellite clock differences of a plurality of nodes based on an n-order Lagrange interpolation method. According to the Lagrangian interpolation formula, the satellite clock difference for a general epoch can be expressed as:
wherein dT is i (t k ) Representing t k Satellite clock difference at time. After the formula (15) is substituted into the formula (1), modeling of clock error parameters is achieved, namely, the original clock error parameters in the formula (1) are represented by a model, then the parameters of the model are solved, and then satellite clock error parameters of all nodes can be solved through observation data.
Step 400: constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations.
Specifically, the expression of the pseudo-range observation equation is:
the expression of the carrier phase observation equation is:
wherein i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; c is the speed of light; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is a tropospheric delay; d, d ISB Is ISB; ds PCC Is a satellite terminal antenna PCC; dr PCC An antenna PCC for a ground monitoring station; d, d r DCB for the receiver; d, d s DCB for satellite end; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity;an initial phase at the receiver end; />The initial phase of the satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; epsilon φ For each remaining error of the carrier phase observations and observation noise.
Step 500: and determining the observation parameters of the GNSS and LNSS observation models of the ground monitoring station based on the Lagrange interpolation algorithm and the track integral function.
For satellite dynamics parameters, the geometric distance ρ between the satellite and the tracking station in equations (16) and (17) can be expressed specifically as:
where (x, y, z) represents the three-dimensional coordinates of the tracking station, and is obtained after linearization:
and carrying the formula (4) and the formula (12) into the formula (19), and estimating the satellite initial moment dynamic parameters so as to obtain a satellite orbit determination result. The observation parameters of the GNSS and LNSS observation models of the ground monitoring station can be obtained.
Step 600: and determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining an LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation.
Step 700: and determining the observation parameters of the LEO satellite-borne GNSS observation model based on the orbit integral function and the Lagrange interpolation algorithm.
For any LEO satellite, the observation basic mode is the same as that of formula (16) and formula (17), the only difference is that the ground station in formula (16) and formula (17) is stationary, and the LEO satellite in the model is moving, and the model needs to be represented by a dynamics model, namely, the geometric distance ρ between the satellite and the tracking station in formula (16) and formula (17) needs to be specifically represented as:
in (x) l ,y l ,z l ) Representing three-dimensional coordinates of LEO satellites, which can be expressed in particular as:
wherein E is X 、E Y 、E Z Respectively representing the orbit integral functions of three coordinate directions of LEO satellite, M l Satellite dynamics parameters representing the initial epoch of the LEO satellite.
The observation equation is linearized, that is, the equation (20) is linearized to obtain:
wherein: dx (dx) l 、dy l 、dz l Similarly, for the partial differential in three coordinate directions of any non-numerical integration node time, the partial differential in three coordinate directions of the LEO satellite at the general time can be respectively expressed as:
wherein E 'is' X 、E' Y 、E' Z An orbit integral partial derivative function representing three coordinate directions of the LEO satellite,representing the initial value of the LEO satellite initial moment dynamics parameter. And substituting the formula (21) and the formula (23) into the formula (22), the initial moment kinetic parameters of the LEO satellite can be estimated, and then the LEO satellite orbit determination result can be obtained.
Step 800: constructing a GNSS and LNSS multisource data fusion observation model according to multisource parameters by adopting a quadratic polynomial modeling and Lagrangian polynomial interpolation method; the multisource parameters include: the BDS-3 and LNSS inter-satellite ranging observation model observation parameters, the ground monitoring station GNSS and LNSS observation model observation parameters, and the LEO satellite on-satellite GNSS observation model observation parameters.
The comprehensive formulas (11), (19) and (22) can be seen that in the fusion of GNSS and LNSS multi-source data, satellite positions of the same satellite at any time can be represented by the same group of dynamic parameters, so that the unified solution of satellite dynamic parameters under different observations is realized.
For satellite clock error parameters, the estimation method in the non-difference observation model is to solve for epoch by epoch according to the data processing sampling interval, and in the inter-satellite non-combination ranging observation model, the method provided by the embodiment adopts two methods of quadratic polynomial modeling and Lagrangian polynomial interpolation to estimate the satellite clock error.
For the quadratic polynomial modeling method, in order to uniformly solve the satellite clock differences in the two observation models, the method sets the data processing sampling interval of the ground tracking station and the fitting arc segment of the satellite clock differences in the inter-satellite non-combination ranging observation model to be the same, namely the starting time of the fitting arc segment is the epoch observation time of the ground tracking station observation data, and when the time parameter is the arc segment starting time, the satellite clock differences of the two observation models can be represented by the parameter C in the joint solution.
The GNSS and LNSS multisource data fusion observation model at this time can be expressed as:
for the Lagrange polynomial interpolation method, in order to uniformly solve the satellite clock differences in the two observation models, the method sets the data processing sampling interval of the ground tracking station to be the same as the Lagrange interpolation interval in the inter-satellite non-combination ranging observation model, and then the method can obtain:
the expression of the GNSS and LNSS multisource data fusion observation model is shown in the formula (25), and is as follows:
/>
wherein L is ij A pseudorange observation from satellite i received for satellite j; ρ ij Is the theoretical distance between satellite i and satellite j; c is the speed of light; dT (dT) j Satellite clock difference for satellite j; t is t j The time of receiving the signal for satellite j; dT (dT) i Satellite clock difference for satellite i; t is t i The moment of transmitting the signal for satellite i;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Error in the propagation of the signal from satellite i to satellite j; i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is a tropospheric delay; d, d r DCB for the receiver; d, d s DCB for satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity; />An initial phase at the receiver end; />The initial phase of the satellite end; epsilon φ Each remaining error and observation noise for the carrier phase observations; (X) i ,Y i ,Z i ) Three-dimensional coordinates for satellite i; (X) j ,Y j ,Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j; t is t k Is the kth time; k is a serial number; n is the order of the Lagrangian interpolation algorithm; l is a sequence number; t is t l Is the first moment.
Step 900: performing parameter estimation on the GNSS and LNSS multi-source data fusion observation model by adopting a least square method to obtain a parameter calculation result; the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
Based on the linearized formula (24) or formula (25), the least square parameter estimation method is utilized to solve the observation equation, and a parameter calculation result is obtained. The least square method may be a method in the prior art, and is not limited thereto.
Example 2
As shown in fig. 3, an embodiment of the present invention provides a GNSS and LNSS data fusion processing system, which includes: the system comprises a data acquisition module 1, a first model construction module 2, a first determination module 3, a second construction module 4, a second determination module 5, a third construction module 6, a third determination module 7, a model construction module 8 and a solving module 9.
The data acquisition module 1 is used for acquiring observation data; the observed data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observations include: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; ground monitoring station observation data includes: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observations of LEO satellites.
The first model construction module 2 is used for constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method according to the inter-satellite ranging observation data.
The first determining module 3 is used for determining the observation parameters of the BDS-3 and LNSS inter-satellite ranging observation model based on a Lagrange interpolation algorithm and a quadratic polynomial method; the observed parameters include: signal transmit time estimates, satellite dynamics parameters, and satellite clock differences.
The second construction module 4 is used for constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations.
The second determining module 5 is configured to determine the observation parameters of the ground monitoring station GNSS and LNSS observation model based on the lagrangian interpolation algorithm and the track integral function.
The third construction module 6 is configured to determine a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determine a LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation.
And a third determining module 7, configured to determine the observation parameters of the LEO satellite-borne GNSS observation model based on the orbit integral function and the lagrangian interpolation algorithm.
The model construction module 8 is used for constructing a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by adopting a method of quadratic polynomial modeling and Lagrangian polynomial interpolation; the multisource parameters include: the BDS-3 and LNSS inter-satellite ranging observation model observation parameters, the ground monitoring station GNSS and LNSS observation model observation parameters, and the LEO satellite on-satellite GNSS observation model observation parameters.
The solving module 9 is used for carrying out parameter estimation on the GNSS and LNSS multisource data fusion observation model by adopting a least square method to obtain a parameter resolving result; the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
Example 3
The embodiment of the invention provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the GNSS and LNSS data fusion processing method in the embodiment 1.
In one embodiment, the present invention further provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements the GNSS and LNSS data fusion processing method of embodiment 1.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. A method for processing GNSS and LNSS data fusion, the method comprising:
obtaining observation data; the observation data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data includes: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observation data of LEO satellites;
according to the inter-satellite ranging observation data, constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method;
based on a Lagrangian interpolation algorithm and a quadratic polynomial method, determining observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model; the observed parameters include: signal emission time estimation value, satellite dynamics parameter and satellite clock error;
constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations;
based on a Lagrange interpolation algorithm and an orbit integral function, determining observation parameters of a GNSS and LNSS observation model of a ground monitoring station;
determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining an LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation;
determining the observation parameters of the LEO satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;
constructing a GNSS and LNSS multisource data fusion observation model according to multisource parameters by adopting a quadratic polynomial modeling and Lagrangian polynomial interpolation method; the multi-source parameters include: BDS-3 and LNSS inter-satellite ranging observation model observation parameters, ground monitoring station GNSS and LNSS observation model observation parameters, and LEO satellite on-satellite GNSS observation model observation parameters;
performing parameter estimation on the GNSS and LNSS multi-source data fusion observation model by adopting a least square method to obtain a parameter calculation result; and the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
2. The method for processing the fusion of the GNSS and LNSS data according to claim 1, wherein the construction of BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method according to the inter-satellite ranging observation data specifically comprises the following steps:
establishing an inter-satellite link ranging observation equation according to the inter-satellite ranging observation data;
establishing a satellite photographed motion equation according to the stress condition in the satellite operation process;
based on a dynamic orbit determination method, determining an inter-satellite ranging observation equation by adopting a Lagrangian interpolation algorithm according to the inter-satellite link ranging observation equation and the satellite photographed motion equation;
and determining a BDS-3 and LNSS inter-satellite ranging observation model according to the inter-satellite ranging observation equation.
3. The GNSS and LNSS data fusion processing method of claim 2 wherein the expression of the inter-satellite link ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; r is (r) i A position vector of the satellite i in a coordinate system; r is (r) j A position vector of the satellite j in a coordinate system; t is t i The moment of transmitting the signal for satellite i; t is t j The time of receiving the signal for satellite j; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; dT (dT) j Satellite clock difference for satellite j;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
4. The GNSS and LNSS data fusion process of claim 2 wherein the expression of the inter-satellite ranging observation equation is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; (X) i ，Y i ，Z i ) Three-dimensional coordinates for satellite i; (X) j ，Y j ，Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; c is the speed of light; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Is the error in the propagation of the signal from satellite i to satellite j.
5. The GNSS and LNSS data fusion process of claim 1 wherein the expression of the pseudorange observation equation is:
the expression of the carrier phase observation equation is:
wherein,i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; c is the speed of light; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is a tropospheric delay; d, d ISB Is ISB; ds PCC Is a satellite terminal antenna PCC; dr PCC An antenna PCC for a ground monitoring station; d, d r DCB for the receiver; d, d s DCB for satellite end; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity;an initial phase at the receiver end; />The initial phase of the satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; epsilon φ For each remaining error of the carrier phase observations and observation noise.
6. The method for processing the GNSS and LNSS data fusion according to claim 1, wherein the expression of the GNSS and LNSS multisource data fusion observation model is:
wherein L is ij A pseudorange observation from satellite i received for satellite j; ρ ij Is the theoretical distance between satellite i and satellite j; c is the speed of light; dT (dT) j Satellite clock difference for satellite j; t is t j The time of receiving the signal for satellite j; dT (dT) i Satellite clock difference for satellite i; t is t i The moment of transmitting the signal for satellite i;signal reception delay for satellite j; />Signal transmission delay for satellite i; />Error in the propagation of the signal from satellite i to satellite j; i is a satellite number; p is a pseudo-range observation value; phi is the carrier phase observation; ρ is the geometric distance between the satellite and the survey station; dt is the receiver clock difference; dT is satellite clock difference; d, d ion Is ionospheric delay; d, d tro Is convection currentLayer delay; d, d r DCB for the receiver; d, d s DCB for satellite end; epsilon P Each remaining error and observation noise for the pseudorange observations; delta r Delay for phase hardware at the receiver; delta s The phase hardware delay of the satellite end; lambda is the carrier wavelength; n is integer ambiguity;an initial phase at the receiver end; />The initial phase of the satellite end; epsilon φ Each remaining error and observation noise for the carrier phase observations; (X) i ，Y i ，Z i ) Three-dimensional coordinates for satellite i; (X) j ，Y j ，Z j ) Three-dimensional coordinates of satellite j; t is t i The moment of transmitting the signal for satellite i; dT (dT) i Satellite clock difference for satellite i; t is t j The time of receiving the signal for satellite j; dT (dT) j Satellite clock difference for satellite j; t is t k Is the kth time; k is a serial number; n is the order of the Lagrangian interpolation algorithm; l is a sequence number; t is t l Is the first moment.
7. A GNSS and LNSS data fusion processing system, the system comprising:
the data acquisition module is used for acquiring observation data; the observation data includes: inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data includes: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises: GNSS observations and LNSS observations; the satellite-borne observation data includes: satellite-borne GNSS observation data of LEO satellites;
the first model construction module is used for constructing BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method according to the inter-satellite ranging observation data;
the first determining module is used for determining the observation parameters of the BDS-3 and LNSS inter-satellite ranging observation model based on a Lagrange interpolation algorithm and a quadratic polynomial method; the observed parameters include: signal emission time estimation value, satellite dynamics parameter and satellite clock error;
the second construction module is used for constructing a ground monitoring station GNSS and LNSS observation model according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation model comprises: pseudo-range observation equations and carrier phase observation equations;
the second determining module is used for determining the observation parameters of the GNSS and LNSS observation models of the ground monitoring station based on the Lagrange interpolation algorithm and the track integral function;
the third construction module is used for determining a satellite-borne GNSS observation equation according to the satellite-borne observation data and determining an LEO satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation;
the third determining module is used for determining the observation parameters of the LEO satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;
the model construction module is used for constructing a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by adopting a method of quadratic polynomial modeling and Lagrangian polynomial interpolation; the multi-source parameters include: BDS-3 and LNSS inter-satellite ranging observation model observation parameters, ground monitoring station GNSS and LNSS observation model observation parameters, and LEO satellite on-satellite GNSS observation model observation parameters;
the solving module is used for carrying out parameter estimation on the GNSS and LNSS multisource data fusion observation model by adopting a least square method to obtain a parameter resolving result; and the parameter calculation result is used for representing the positioning navigation and time service of the satellite system.
8. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the GNSS and LNSS data fusion processing method according to any of claims 1 to 6.
9. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the GNSS and LNSS data fusion processing method according to any of claims 1 to 6.
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