A GNSS and LNSS data fusion processing method and system

Abstract

The application 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 a BDS-3 and LNSS inter-satellite ranging observation model, a ground monitoring station GNSS and LNSS observation model and a LEO satellite on-board GNSS observation model according to acquired observation data; then, observation parameters of each observation model are determined; a quadratic polynomial modeling and Lagrange polynomial interpolation method is adopted to construct a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters; a least square method is adopted to perform parameter estimation on the GNSS and LNSS multi-source data fusion observation model, so that a parameter solution result is obtained; and the parameter solution result is used for representing positioning, navigation and timing of a satellite system; and the application can provide high-precision and reliable navigation and positioning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data fusion, in particular to a GNSS and LNSS data fusion processing method and system. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) is widely used in various fields due to its all-weather, high real-time, high precision and other advantages. There are currently four GNSSs providing services, namely the Global Positioning System (GPS) of the United States, the GLONASS system of Russia, the Galileo system of the European Union and the Beidou satellite navigation system (BDS) of China. BDS is independently constructed and operated by China, and is composed of three types of satellites, namely geostationary orbit (GEO), inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO). The overall construction is divided into three stages, namely verification system (BDS-1), extended regional navigation system (BDS-2) and global navigation system (BDS-3). BDS-3 officially provides services for global users, marking the completion of the three-step strategy for BDS construction.

[0003] With the increasing demand of users for navigation and positioning, high precision, high reliability, fast convergence and other elements have attracted more and more attention of GNSS users, which has promoted the development of Low Earth Orbit (LEO) navigation satellite system. Compared with GNSS satellites, LEO satellites have lower orbit height, which makes the LEO navigation satellite system, i.e. Low Orbit Navigation Satellite System (LNSS), have the following advantages: 1) fast change of geometric structure on the ground, which significantly improves the user positioning convergence time; 2) high signal landing power, which improves the anti-interference ability of navigation and positioning; 3) strong satellite-ground communication capability, which effectively enhances the GNSS service capability. In addition, for BDS, the dense LEO satellite network can be used as a mobile monitoring station, which effectively makes up for the problem that the Chinese self-controllable ground monitoring stations cannot be distributed globally, and significantly improves the BDS satellite orbit and clock error solving accuracy based on regional ground monitoring stations.

[0004] The coexistence of multiple navigation systems has given rise to the demand for multiple observation data fusion processing. With the development of LNSS, on the basis of original ground monitoring station GNSS observation and BDS-3 satellite inter-satellite link (ISL) observation, the addition of LNSS adds ground monitoring station LNSS observation, LEO satellite on-board GNSS observation, LEO satellite ISL observation and other signal sources. How to establish a multi-source data fusion processing method based on GNSS and LNSS multi-source observation data is crucial for ensuring the construction of LNSS to provide high-precision, high-reliability, fast-convergence positioning, navigation and timing (PNT) services. SUMMARY

[0005] The application aims to provide a GNSS and LNSS data fusion processing method and system to provide high-precision and reliable navigation positioning.

[0006] To achieve the above-mentioned purpose, the application provides the following solutions.

[0007] A GNSS and LNSS data fusion processing method, the method comprising:

[0008] acquiring observation data; the observation data comprising inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data comprising BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprising GNSS observation data and LNSS observation data; the satellite-borne observation data comprising satellite-borne GNSS observation data of a LEO satellite;

[0009] 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;

[0010] determining 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 observation parameters comprising signal transmission time point estimates, satellite dynamic parameters and satellite clock errors;

[0011] 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 comprising a pseudo-range observation equation and a carrier phase observation equation;

[0012] determining observation parameters of the ground monitoring station GNSS and LNSS observation model based on a Lagrange interpolation algorithm and an orbit integral function;

[0013] determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining a LEO satellite satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation;

[0014] determining observation parameters of the LEO satellite satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;

[0015] constructing a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by using a quadratic polynomial modeling and Lagrange polynomial interpolation method; the multi-source parameters comprising observation parameters of the BDS-3 and LNSS inter-satellite ranging observation model, observation parameters of the ground monitoring station GNSS and LNSS observation model, and observation parameters of the LEO satellite satellite-borne GNSS observation model;

[0016] The least square method is used to estimate parameters of the GNSS and LNSS multi-source data fusion observation model, and parameter solution results are obtained; the parameter solution results are used to represent positioning, navigation and timing of a satellite system.

[0017] Optionally, according to the inter-satellite ranging observation data, a BDS-3 and LNSS inter-satellite ranging observation model is constructed based on a dynamic orbit determination method, and specifically includes:

[0018] An inter-satellite link ranging observation equation is established according to the inter-satellite ranging observation data;

[0019] A satellite forced motion equation is established according to force conditions in a satellite operation process;

[0020] Based on the dynamic orbit determination method, the inter-satellite ranging observation equation is determined by using a Lagrange interpolation algorithm according to the inter-satellite link ranging observation equation and the satellite forced motion equation;

[0021] The BDS-3 and LNSS inter-satellite ranging observation model is determined according to the inter-satellite ranging observation equation.

[0022] Optionally, the expression of the inter-satellite link ranging observation equation is:

[0023]

[0024] Wherein, L ij is a pseudo-range observation received by satellite j from satellite i; r i is a position vector of satellite i in a coordinate system; r j is a position vector of satellite j in the coordinate system; t i is a time when satellite i transmits a signal; t j is a time when satellite j receives the signal; c is a light speed; dT i is a satellite clock error of satellite i; dT j is a satellite clock error of satellite j; is a signal receiving time delay of satellite j; is a signal transmitting time delay of satellite i; is an error in signal propagation from satellite i to satellite j.

[0025] Optionally, the expression of the inter-satellite ranging observation equation is:

[0026]

[0027] Wherein, L ij is a pseudo-range observation received by satellite j from satellite i; (X i , Y i , Z i ) is a three-dimensional coordinate of satellite i; (Xj Y j Z j ) is the three-dimensional coordinate of satellite j; t i is the time when satellite i transmits the signal; c is the speed of light; dT i is the satellite clock error of satellite i; t j is the time when satellite j receives the signal; dT j is the satellite clock error of satellite j; is the signal receiving time delay of satellite j; is the signal transmitting time delay of satellite i; is the error in the signal propagation from satellite i to satellite j.

[0028] Optionally, the expression of the pseudo-range observation equation is:

[0029]

[0030] The expression of the carrier phase observation equation is:

[0031]

[0032] wherein i is the satellite number; P is the pseudo-range observation value; φ is the carrier phase observation value; ρ is the geometric distance between the satellite and the station; c is the speed of light; dt is the receiver clock error; dT is the satellite clock error; d ion is the ionospheric delay; d tro is the tropospheric delay; d ISB is the ISB; ds PCC is the satellite end antenna PCC; dr PCC is the ground monitoring station antenna PCC; d r is the receiver end DCB; d s is the satellite end DCB; δ r is the receiver end phase hardware delay; δ s is the satellite end phase hardware delay; λ is the carrier wavelength; N is the integer ambiguity; is the initial phase of the receiver end; is the initial phase of the satellite end; ε P is each residual error and observation noise of the pseudo-range observation value; ε φ is each residual error and observation noise of the carrier phase observation value.

[0033] Optionally, the expression of the GNSS and LNSS multi-source data fusion observation model is:

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] where L ij is the pseudorange observation received by satellite j from satellite i; p ij is the theoretical distance between satellite i and satellite j; c is the speed of light; dT j is the satellite clock error of satellite j; t j is the time when the signal is received by satellite j; dT i is the satellite clock error of satellite i; t i is the time when the signal is transmitted by satellite i; is the signal receiving delay of satellite j; is the signal transmitting delay of satellite i; is the error in the signal propagation from satellite i to satellite j; i is the satellite number; P is the pseudorange observation; f is the carrier phase observation; p is the geometric distance between the satellite and the station; dt is the receiver clock error; dT is the satellite clock error; d ion is the ionospheric delay; d tro is the tropospheric delay; d r is the DCB at the receiver end; d s is the DCB at the satellite end; e P is the residual error and observation noise of the pseudorange observation; d r is the phase hardware delay at the receiver end; d s is the phase hardware delay at the satellite end; l is the carrier wavelength; N is the integer ambiguity; is the initial phase at the receiver end; is the initial phase at the satellite end; e φ is the residual error and observation noise of the carrier phase observation; (X i , Y i , Z i ) is the three-dimensional coordinate of satellite i; (X j , Y j , Z j ) is the three-dimensional coordinate of satellite j; t i is the time when the signal is transmitted by satellite i; dT i is the satellite clock error of satellite i; t jis the time instant when the signal is received by satellite j; dT j is the satellite clock bias of satellite j; t k is the kth time instant; k is the serial number; n is the order of the Lagrange interpolation algorithm; l is the serial number; t l is the lth time instant.

[0043] A GNSS and LNSS data fusion processing system, the system comprising:

[0044] a data acquisition module for acquiring observation data; the observation data comprising: inter-satellite ranging observation data, ground monitoring station observation data and on-board observation data; the inter-satellite ranging observation data comprising: BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprising: GNSS observation data and LNSS observation data; the on-board observation data comprising: on-board GNSS observation data of a LEO satellite;

[0045] a first model construction module for 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;

[0046] a first determination module for determining 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 observation parameters comprising: signal transmission time instant estimate, satellite dynamic parameters and satellite clock bias;

[0047] a second construction module 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 comprising: a pseudo-range observation equation and a carrier phase observation equation;

[0048] a second determination module for determining observation parameters of the ground monitoring station GNSS and LNSS observation model based on a Lagrange interpolation algorithm and an orbit integral function;

[0049] a third construction module for determining an on-board GNSS observation equation according to the on-board observation data, and determining a LEO satellite on-board GNSS observation model according to the on-board GNSS observation equation;

[0050] a third determination module for determining observation parameters of the LEO satellite on-board GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm;

[0051] The model construction module is configured to construct a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by using a quadratic polynomial modeling method and a Lagrange polynomial interpolation method, wherein the multi-source parameters include observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model, observation parameters of a GNSS and LNSS observation model of a ground monitoring station, and observation parameters of a LEO satellite on-board GNSS observation model.

[0052] The solving module is configured to perform parameter estimation on the GNSS and LNSS multi-source data fusion observation model by using a least square method to obtain a parameter solution result, wherein the parameter solution result is used to represent positioning, navigation and timing of a satellite system.

[0053] An electronic device includes a memory configured to store a computer program and a processor configured to execute the computer program to enable the electronic device to perform the GNSS and LNSS data fusion processing method.

[0054] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the GNSS and LNSS data fusion processing method.

[0055] According to the embodiments of the present application, the following technical effects are provided.

[0056] The present application provides a GNSS and LNSS data fusion processing method and system, which constructs a BDS-3 and LNSS inter-satellite ranging observation model, a GNSS and LNSS observation model of a ground monitoring station, and a LEO satellite on-board GNSS observation model according to obtained observation data; then determines observation parameters of each observation model; constructs a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by using a quadratic polynomial modeling method and a Lagrange polynomial interpolation method; performs parameter estimation on the GNSS and LNSS multi-source data fusion observation model by using a least square method to obtain a parameter solution result; and the parameter solution result is used to represent positioning, navigation and timing of a satellite system; and the present application can provide high-precision and reliable navigation and positioning. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0058] Figure 1 The flowchart of the GNSS and LNSS data fusion processing method provided by the embodiments of the present application is shown.

[0059] Figure 2 The technical flow block diagram of the GNSS and LNSS data fusion processing method provided by the embodiment of the present application is shown in the figure.

[0060] Figure 3 The structural diagram of the GNSS and LNSS data fusion processing system provided by the embodiment of the present application is shown in the figure.

[0061] Symbol explanation:

[0062] Data acquisition module-1, first model construction module-2, first determination module-3, second construction module-4, second determination module-5, third construction module-6, third determination module-7, model construction module-8, solving module-9. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0064] The purpose of the present application is to provide a GNSS and LNSS data fusion processing method and system to provide high-precision and reliable navigation positioning.

[0065] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0066] Embodiment 1

[0067] As shown in Figure 1 and Figure 2 The embodiment of the present application provides a GNSS and LNSS data fusion processing method, which comprises:

[0068] Step 100: acquiring observation data; the observation data comprises inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data comprises BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises GNSS observation data and LNSS observation data; the satellite-borne observation data comprises satellite-borne GNSS observation data of a LEO satellite.

[0069] Step 200: 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.

[0070] Wherein, according to the inter-satellite ranging observation data, the inter-satellite ranging observation model of BDS-3 and LNSS is constructed based on the dynamic orbit determination method, which specifically includes:

[0071] According to the inter-satellite ranging observation data, the inter-satellite link ranging observation equation is established, and the satellite forced motion equation is established according to the force condition in the satellite operation process; based on the dynamic orbit determination method, the inter-satellite ranging observation equation is determined by using the Lagrange interpolation algorithm according to the inter-satellite link ranging observation equation and the satellite forced motion equation.

[0072] Specifically, the expression of the inter-satellite link ranging observation equation is:

[0073]

[0074] Wherein, L ij is the pseudo-range observation of satellite j received from satellite i; r i is the position vector of satellite i in the coordinate system; r i is the position vector of satellite j in the coordinate system; t i is the time when satellite i transmits the signal; t j is the time when satellite j receives the signal; c is the speed of light; dT i is the satellite clock error of satellite i; dT j is the satellite clock error of satellite j; is the signal receiving delay of satellite j; is the signal transmitting delay of satellite i; is the error in the signal propagation process from satellite i to satellite j.

[0075] The expression of the inter-satellite ranging observation equation is:

[0076]

[0077] Wherein, L ij is the pseudo-range observation of satellite j received from satellite i; (X i , Y i , Z i ) are three-dimensional coordinates of satellite i; (X j , Y j , Z j ) are three-dimensional coordinates of satellite j; t i is the time when satellite i transmits the signal; c is the speed of light; dT i is the satellite clock error of satellite i; t j is the time when satellite j receives the signal; dT j is the satellite clock error of satellite j; is the signal receiving delay of satellite j; is the signal transmitting delay of satellite i; Error of signal propagation from satellite i to satellite j.

[0078] The BDS-3 and LNSS inter-satellite ranging observation models are determined according to the inter-satellite ranging observation equation.

[0079] Step 300: Based on the Lagrange interpolation algorithm and the quadratic polynomial method, the observation parameters of the BDS-3 and LNSS inter-satellite ranging observation models are determined; the observation parameters include: signal transmission time estimate, satellite dynamic parameters and satellite clock error.

[0080] In practical applications, the inter-satellite link ranging observation equation between two satellites can be expressed as:

[0081]

[0082] At present, the dynamic orbit determination method is still the mainstream method for satellite orbit determination, and the basic idea is to establish a satellite perturbed motion equation according to the force acting on the satellite during its operation, and then solve the satellite dynamic parameters through the observation equation established by the observation data.

[0083] When the signal transmission time of satellite i and the signal reception time of satellite j are the node times of numerical integration, the positions of satellite i and satellite j at each numerical integration node are represented by the dynamic parameters of the satellite at the initial epoch; in the inter-satellite link observation, the observation data used for satellite orbit determination come from the inter-satellite ranging data. Based on the dynamic orbit determination method, the positions of satellite i and satellite j at each numerical integration node can be represented by the dynamic parameters of the satellite at the initial epoch, i.e.:

[0084]

[0085]

[0086] In the formula, (X i ,Y i ,Z i ) and (X j ,Y j ,Z j ) represent the three-dimensional coordinates of satellites i and j respectively, t k is the kth time, at which the node time of numerical integration is represented, F X , F Y , F Z represent the orbit integral functions of satellite i in three coordinate directions, G X , G Y , G Z represent the orbit integral functions of satellite j in three coordinate directions, and M i and M j represent the satellite dynamic parameters of satellites i and j at the initial epoch.

[0087] When the signal transmission time of satellite i and the signal reception time of satellite j are not the node times of numerical integration, the positions of satellite i and satellite j at the general time are represented.

[0088] For the ranging observation of the inter-satellite link, the signal transmission time of satellite i and the signal reception time of satellite j can not be the node times of numerical integration. Considering that the satellite orbit is relatively smooth, for the satellite positions at any non-numerical integration node time, the satellite positions at several numerical integration node times near the time can be obtained based on the n-order Lagrange interpolation method. According to the Lagrange interpolation formula, the positions of satellite i and satellite j at the general time can be respectively represented as:

[0089]

[0090]

[0091] In the formula, t l is the lth time, and here represents the node time of numerical integration.

[0092] In the calculation process of formula (4) and formula (5), formula (2) and formula (3) are referenced. After formula (4) and (5) are substituted into formula (1), the inter-satellite ranging observation equation can be represented as:

[0093]

[0094] As can be seen from formula (6), in the processing method of various parameters and errors in the inter-satellite non-combined ranging observation model, the signal transmission and reception time delay parameter estimation method and the error correction method of the satellite antenna phase center and the relativistic effect are the same as those in the inter-satellite combined ranging observation model. Therefore, the key technology of establishing the inter-satellite non-combined ranging observation model lies in how to estimate the signal transmission time, the satellite orbit error and the satellite clock error.

[0095] Based on the inter-satellite link ranging observation equation (formula 6), the signal transmission time, the satellite orbit error and the satellite clock error are considered to solve the initial time dynamic parameters of the satellite, the quadratic term model parameters of each arc segment and the satellite clock error parameters of each node.

[0096] For the estimation algorithm of the signal transmission time of the inter-satellite link observation, the estimation algorithm of the signal transmission time in the GNSS observation can be imitated. The signal transmission time in the inter-satellite link observation can be represented as:

[0097]

[0098] In the formula, Δt ij represents the signal propagation time delay from satellite i to satellite j.

[0099] In formula (7), the satellite clock error, signal transmission time delay and signal receiving time delay can be represented by the results of the previous parameter estimation. If it is the first parameter estimation cycle, the satellite clock error is obtained from the broadcast ephemeris, and the signal transmission and receiving time delay can be substituted into the existing prior value because they are relatively stable. For the initial value of the signal propagation time delay, it can be represented by the inter-satellite ranging observation value, that is:

[0100] Δt ij = L ij / c (8)

[0101] After substituting formula (8) into formula (7), the signal transmission time can be obtained, and another value of the signal propagation time delay can be calculated by the orbit integral function, that is:

[0102]

[0103] If |Δt ij -Δt ij |>10 -9 , then Δt ij is substituted into formula (7) as the new signal propagation time delay estimation value. Through continuous iteration of formula (7) and formula (9), the estimation value of the signal transmission time t i is obtained until the requirement is met.

[0104] Based on the signal transmission time obtained by iteration calculation, the theoretical distance ρ ij between satellites can be represented as:

[0105]

[0106] After linearization, we have:

[0107]

[0108] In the formula, dX i , dY i , dZ i and dX j , dY j , dZ j represent the partial derivatives of satellite i and satellite j in three coordinate directions, respectively. Similarly, for the partial derivatives of any non-numerical integral node time in three coordinate directions, the partial derivatives of several numerical integral nodes near the time in the corresponding coordinate directions can be obtained based on the n-order Lagrange interpolation method. According to the Lagrange interpolation formula, the partial derivatives of satellite i and satellite j in three coordinate directions at a general time can be represented as:

[0109]

[0110]

[0111] where f' X , F' Y , F' Z represent the orbital integral partial differential functions of satellite i in three coordinate directions, G' X , G' Y , G' Z represent the orbital integral partial differential functions of satellite j in three coordinate directions, and represent the initial values of the dynamic parameters of satellite i and satellite j at the initial time, respectively. Substituting the equations (4) and (5) and the equations (12) and (13) into the equation (11), the linearized observation equation is obtained after substitution, and the dynamic parameters of the satellite at the initial time can be estimated on the linearized observation equation, and then the satellite orbit determination result is obtained.

[0112] Since the current inter-satellite links adopt the time division multiple access mode, the signal transmission and reception time of different inter-satellite link observations near the time t k is basically different. For a satellite, if the satellite clock bias is estimated at each signal reception and transmission time, the number of parameters will be too large. The satellite clock bias in the inter-satellite non-combined ranging observation model is processed by the following two methods.

[0113] Regarding the quadratic polynomial method:

[0114] The satellite clock bias of the same satellite at different epochs is represented by the same model, but considering the poor stability of the satellite clock bias, it cannot be represented by a model in the entire observation arc segment like the satellite orbit, and the entire observation arc segment needs to be divided into several segments, and the satellite clock bias of each arc segment is represented by different models. In this method, the quadratic term model is used as the basic model, and for the current satellite clock accuracy, the quadratic term model can effectively simulate the change of the satellite clock bias within a certain arc length. For satellite i, the satellite clock bias at the time t i in an arc segment can be represented as:

[0115] D T i (t i ) = A i (t i -t k ) 2 +B i (t i -t k )+C i (14)

[0116] where A i , B i , C i represent the to-be-solved parameters of the quadratic term model, tk represents the starting time of the arc segment. After substituting equation (14) into equation (1), the modeling of the clock difference parameters is realized, that is, the original clock difference parameters in equation (1) are represented by a model, and then the parameters of the model are solved, that is, the quadratic term model parameters of each arc segment are solved through the observation data. For the selection strategy of the length of the arc segment, the method provided in the embodiment adopts an integral multiple of 3s, and the specific length of the arc segment is obtained by comparing the relationship among the amount of measured data, the amount of to-be-estimated parameters, and the data processing accuracy.

[0117] Regarding the Lagrange interpolation method:

[0118] Considering that the change of the satellite clock difference is relatively stable, the satellite clock differences of several equally spaced nodes (such as 30s intervals) in the observation arc segment can be selected as the solving parameters, and the satellite clock differences of the remaining epochs can be obtained by interpolating the satellite clock differences of the several nodes based on the n-order Lagrange interpolation method. According to the Lagrange interpolation formula, the satellite clock difference of a general epoch can be represented as:

[0119]

[0120] In equation (15), dT i (t k ) represents the satellite clock difference at t k . After substituting equation (15) into equation (1), the modeling of the clock difference parameters is realized, that is, the original clock difference parameters in equation (1) are represented by a model, and then the parameters of the model are solved, that is, the satellite clock difference parameters of each node are solved through the observation data.

[0121] 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 a pseudo-range observation equation and a carrier phase observation equation.

[0122] Specifically, the expression of the pseudo-range observation equation is:

[0123]

[0124] The expression of the carrier phase observation equation is:

[0125]

[0126] Wherein, i is the satellite number; P is the pseudo-range observation value; φ is the carrier phase observation value; ρ is the geometric distance between the satellite and the station; c is the speed of light; dt is the receiver clock difference; dT is the satellite clock difference; d ion is the ionospheric delay; d tro is the tropospheric delay; d ISB is the ISB; dsPCC PCC;dr is the PCC at the receiver end PCC PCC;d is the PCC at the ground monitoring station r DCB;d is the DCB at the receiver end s DCB;δ is the DCB at the satellite end r φd is the phase hardware delay at the receiver end s φδ is the phase hardware delay at the satellite end λ is the carrier wavelength; N is the integer ambiguity φd0 is the initial phase at the receiver end φδ0 is the initial phase at the satellite end ε is the observation noise P ε is the residual error and observation noise of the pseudorange observation φ ε is the residual error and observation noise of the carrier phase observation

[0127] Step 500: Based on the Lagrange interpolation algorithm and the orbit integral function, the observation parameters of the ground monitoring station GNSS and LNSS observation model are determined.

[0128] For the satellite dynamic parameters, in the formula (16) and (17), the geometric distance ρ between the satellite and the tracking station can be specifically expressed as:

[0129]

[0130] In the formula, (x, y, z) represents the three-dimensional coordinates of the tracking station, and after linearization, we get:

[0131]

[0132] The formula (4) and the formula (12) are brought into the formula (19), the satellite initial time dynamic parameters are estimated, and then the satellite orbit determination result is obtained. That is, the observation parameters of the ground monitoring station GNSS and LNSS observation model are obtained.

[0133] Step 600: According to the on-board observation data, the on-board GNSS observation equation is determined, and the LEO satellite on-board GNSS observation model is determined according to the on-board GNSS observation equation.

[0134] Step 700: Based on the orbit integral function and the Lagrange interpolation algorithm, the observation parameters of the LEO satellite on-board GNSS observation model are determined.

[0135] For any LEO satellite, the observation basic mode is the same as the formula (16) and the formula (17), the only difference is that the ground station is fixed in the formula (16) and the formula (17), while the LEO satellite is mobile in this model, which needs to be expressed by a dynamic model, that is, the geometric distance ρ between the satellite and the tracking station in the formula (16) and the formula (17) needs to be specifically expressed as:

[0136]

[0137] wherein (x l ,y l ,z l ) represents the three-dimensional coordinates of the LEO satellite, and can be specifically represented as:

[0138]

[0139] wherein E X , E Y , and E Z represent the integral functions of the three coordinate directions of the LEO satellite, and M l represents the satellite dynamic parameters of the initial epoch of the LEO satellite.

[0140] The observation equation is linearized, that is, linearization of equation (20) is performed to obtain:

[0141]

[0142] wherein dx l , dy l , and dz l represent the partial derivatives of the LEO satellite in the three coordinate directions. Similarly, for the partial derivatives of the three coordinate directions at any non-numerical integral node time, the partial derivatives of the corresponding coordinate directions of several numerical integral nodes near the time can be obtained based on the n-order Lagrange interpolation method, and according to the Lagrange interpolation formula, the partial derivatives of the LEO satellite in the three coordinate directions at a general time can be represented as:

[0143]

[0144] wherein E' X , E' Y , and E' Z represent the partial derivative functions of the three coordinate directions of the LEO satellite, represents the initial value of the dynamic parameters at the initial time of the LEO satellite. Equation (21) and equation (23) are substituted into equation (22) to estimate the dynamic parameters at the initial time of the LEO satellite, and then the orbit determination result of the LEO satellite is obtained.

[0145] Step 800: a method of quadratic polynomial modeling and Lagrange polynomial interpolation is adopted to construct a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters; the multi-source parameters include: observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model, observation parameters of a ground monitoring station GNSS and LNSS observation model, and observation parameters of a LEO satellite on-board GNSS observation model.

[0146] As can be seen from formula (11), formula (19) and formula (22), in GNSS and LNSS multi-source data fusion, the satellite position of the same satellite at any time can be represented by the same set of dynamic parameters, and then the unified solution of the satellite dynamic parameters under different observations is realized.

[0147] For the satellite clock error parameter, the estimation method in the non-difference observation model is to solve it according to the data processing sampling interval per epoch, and in the inter-satellite non-combined ranging observation model, the method provided in the embodiment estimates the satellite clock error by using two methods of quadratic polynomial modeling and Lagrange polynomial interpolation.

[0148] For the quadratic polynomial modeling method, in order to uniformly solve the satellite clock error in the two observation models, the data processing sampling interval of the ground tracking station and the fitting arc segment of the satellite clock error in the inter-satellite non-combined ranging observation model are set to be the same, that is, the starting time of the fitting arc segment is the epoch observation time of the ground tracking station observation data. As can be seen from formula (14), when the time parameter is the starting time of the arc segment, at this time in the joint solution, the satellite clock error of the two observation models can be represented by the parameter C.

[0149] At this time, the GNSS and LNSS multi-source data fusion observation model can be expressed as:

[0150]

[0151] For the Lagrange polynomial interpolation method, in order to uniformly solve the satellite clock error in the two observation models, the data processing sampling interval of the ground tracking station and the Lagrange interpolation interval in the inter-satellite non-combined ranging observation model are set to be the same, and then the following can be obtained:

[0152] The expression of the GNSS and LNSS multi-source data fusion observation model is shown in formula (25) as follows:

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161] wherein, L ij is the pseudorange observation received by satellite j from satellite i; p ij is the theoretical distance between satellite i and satellite j; c is the speed of light; dT j is the satellite clock bias of satellite j; t j is the time when the signal is received by satellite j; dT i is the satellite clock bias of satellite i; t i is the time when the signal is transmitted by satellite i; is the signal receiving time delay of satellite j; is the signal transmitting time delay of satellite i; is the error in the signal propagation from satellite i to satellite j; i is the satellite number; P is the pseudorange observation; φ is the carrier phase observation; p is the geometric distance between the satellite and the station; dt is the receiver clock bias; dT is the satellite clock bias; d ion is the ionospheric delay; d tro is the tropospheric delay; d r is the DCB at the receiver end; d s is the DCB at the satellite end; ε P is each residual error and observation noise of the pseudorange observation; δ r is the phase hardware delay at the receiver end; δ s is the phase hardware delay at the satellite end; λ is the carrier wavelength; N is the integer ambiguity; is the initial phase at the receiver end; is the initial phase at the satellite end; ε φ is each residual error and observation noise of the carrier phase observation; (X i , Y i , Z i ) is the three-dimensional coordinate of satellite i; (X j , Y j , Z j ) is the three-dimensional coordinate of satellite j; t i is the time when the signal is transmitted by satellite i; dT i is the satellite clock bias of satellite i; t j is the time when the signal is received by satellite j; dT j is the satellite clock bias of satellite j; t k is the kth time; k is the serial number; n is the order of the Lagrange interpolation algorithm; l is the serial number; t l is the lth time.

[0162] Step 900: performing parameter estimation on the GNSS and LNSS multi-source data fusion observation model by using the least square method to obtain a parameter solution result; the parameter solution result is used to represent the positioning, navigation and timing of the satellite system.

[0163] Based on the linearized formula (24) or formula (25), the observation equation is solved by using the least square parameter estimation method to obtain the parameter solution result. The least square method can be obtained by using the method in the prior art, and is not limited here.

[0164] Embodiment 2

[0165] As Figure 3 shown, the embodiment of the application provides a GNSS and LNSS data fusion processing system, which 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 solution module 9.

[0166] The data acquisition module 1 is used for acquiring observation data; the observation data comprises inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data comprises BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises GNSS observation data and LNSS observation data; and the satellite-borne observation data comprises satellite-borne GNSS observation data of a LEO satellite.

[0167] The first model construction module 2 is used for 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.

[0168] The first determination module 3 is used for determining 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 observation parameters comprise a signal transmission time estimate, satellite dynamic parameters and satellite clock errors.

[0169] 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 a pseudo-range observation equation and a carrier phase observation equation.

[0170] The second determination module 5 is used for determining observation parameters of the ground monitoring station GNSS and LNSS observation model based on a Lagrange interpolation algorithm and an orbit integral function.

[0171] The third construction module 6 is used for determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining a LEO satellite satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation.

[0172] The third determination module 7 is used for determining observation parameters of the LEO satellite satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm.

[0173] The model construction module 8 is configured to construct a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by using a method of quadratic polynomial modeling and Lagrange polynomial interpolation; the multi-source parameters include observation parameters of a BDS-3 and LNSS inter-satellite ranging observation model, observation parameters of a ground monitoring station GNSS and LNSS observation model, and observation parameters of a LEO satellite on-board GNSS observation model.

[0174] The solving module 9 is configured to perform parameter estimation on the GNSS and LNSS multi-source data fusion observation model by using a least square method to obtain a parameter solution result; the parameter solution result is used to represent positioning, navigation and timing of a satellite system.

[0175] Embodiment 3

[0176] The embodiment of the present application provides an electronic device, including a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to make the electronic device execute the GNSS and LNSS data fusion processing method in embodiment 1.

[0177] In an embodiment, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the GNSS and LNSS data fusion processing method in embodiment 1.

[0178] In the specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between each embodiment can be referred 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 related parts can be referred to the method part.

[0179] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above embodiment description is only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A GNSS and LNSS data fusion processing method, characterized in that, The method comprises: acquiring observation data; the observation data comprises inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data comprises BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises GNSS observation data and LNSS observation data; and the satellite-borne observation data comprises satellite-borne GNSS observation data of a LEO satellite; 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; determining 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 observation parameters comprise a signal transmission time estimate, satellite dynamic parameters and satellite clock error; wherein the quadratic polynomial method adopts a quadratic term model to simulate satellite clock error changes within an arc length; For satellites In one arc segment The satellite clock error at the time is expressed as: ； wherein, , , denote the parameters to be determined of the quadratic model, denotes the start time of the arc segment; is the time of satellite signal transmission; is the satellite clock error of satellite . 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 a pseudo-range observation equation and a carrier phase observation equation; determining observation parameters of the ground monitoring station GNSS and LNSS observation model based on a Lagrange interpolation algorithm and an orbit integral function; determining a satellite-borne GNSS observation equation according to the satellite-borne observation data, and determining a LEO satellite satellite-borne GNSS observation model according to the satellite-borne GNSS observation equation; determining observation parameters of the LEO satellite satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm; constructing a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by adopting a quadratic polynomial modeling and Lagrange polynomial interpolation method; the multi-source parameters comprise observation parameters of the BDS-3 and LNSS inter-satellite ranging observation model, observation parameters of the ground monitoring station GNSS and LNSS observation model, and observation parameters of the LEO satellite satellite-borne GNSS observation model; performing parameter estimation on the GNSS and LNSS multi-source data fusion observation model by adopting a least square method to obtain a parameter solution result; the parameter solution result is used to represent positioning, navigation and timing of a satellite system.

2. The GNSS and LNSS data fusion processing method according to claim 1, characterized in that, 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, specifically comprising: establishing an inter-satellite link ranging observation equation according to the inter-satellite ranging observation data; establishing a satellite perturbation motion equation according to force conditions in a satellite operation process; determining an inter-satellite ranging observation equation by adopting a Lagrange interpolation algorithm based on the inter-satellite link ranging observation equation and the satellite perturbation motion equation according to a dynamic orbit determination method; 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 according to claim 2, characterized in that, an expression of the inter-satellite link ranging observation equation is: ； wherein is a satellite received pseudorange observation from a satellite ; is a satellite position vector in a coordinate system ; is a satellite position vector in a coordinate system ; is a satellite time of reception of a signal ; is a light speed ; is a satellite satellite clock error ; is a satellite signal reception delay ; is a satellite signal transmission delay ; 4. The GNSS and LNSS data fusion processing method according to claim 2, characterized in that, an expression of the inter-satellite ranging observation equation is: ； wherein, is a satellite received pseudorange observation from a satellite ; is a three-dimensional coordinate of a satellite ; is a three-dimensional coordinate of a satellite ; is a time instant of signal transmission by a satellite ; is a speed of light ; is a satellite clock bias of a satellite ; is a time instant of signal reception by a satellite ; is a satellite clock bias of a satellite ; is a signal reception delay of a satellite ; is a signal transmission delay of a satellite ; is an error in signal propagation from a satellite to a satellite .

5. The GNSS and LNSS data fusion processing method of claim 1, wherein, an expression of the pseudo-range observation equation is: ； an expression of the carrier phase observation equation is: ； wherein, is a satellite number; is a pseudo-range observation; is a carrier phase observation; is a geometric distance between a satellite and a station; is a light speed; is a receiver clock bias; is a satellite clock bias; is an ionosphere delay; is a troposphere delay; is an ISB; is a satellite end antenna PCC; is a ground monitoring station antenna PCC; is a receiver end DCB; is a satellite end DCB; is a receiver end phase hardware delay; is a satellite end phase hardware delay; is a carrier wavelength; is an integer ambiguity; is a receiver end initial phase; is a satellite end initial phase; is each residual error and observation noise of the pseudo-range observation; is each residual error and observation noise of the carrier phase observation.

6. The GNSS and LNSS data fusion processing method of claim 1, wherein, an expression of the GNSS and LNSS multi-source data fusion observation model is: ； wherein, is the satellite is the pseudorange observation received from the satellite ; is the theoretical distance between the satellite and the satellite ; is the speed of light; is the satellite clock bias of the satellite ; is the time instant when the signal is received by the satellite ; is the satellite clock bias of the satellite ; is the time instant when the signal is transmitted by the satellite ; is the signal reception delay of the satellite ; is the signal transmission delay of the satellite ; is the error in the signal propagation from the satellite to the satellite ; is the satellite number; is the pseudorange observation; is the carrier phase observation; is the geometric distance between the satellite and the station; is the receiver clock bias; is the satellite clock bias; is the ionospheric delay; is the tropospheric delay; is the DCB at the receiver end; is the DCB at the satellite end; is the residual error and observation noise of the pseudorange observation; is the phase hardware delay at the receiver end; is the phase hardware delay at the satellite end; is the carrier wavelength; is the integer ambiguity; is the initial phase at the receiver end; is the initial phase at the satellite end; is the residual error and observation noise of the carrier phase observation; is the three-dimensional coordinate of the satellite ; is the three-dimensional coordinate of the satellite ; is the time instant when the signal is transmitted by the satellite ; is the satellite clock bias of the satellite ; is the time instant when the signal is received by the satellite ; Satellite Satellite clock error; Satellite Time; Sequence number; Order of Lagrange interpolation algorithm; Sequence number; Satellite Time.

7. A GNSS and LNSS data fusion processing system characterized by, The system comprises: a data acquisition module, configured to acquire observation data; the observation data comprises inter-satellite ranging observation data, ground monitoring station observation data and satellite-borne observation data; the inter-satellite ranging observation data comprises BDS-3 inter-satellite ranging observation data and LNSS inter-satellite ranging observation data; the ground monitoring station observation data comprises GNSS observation data and LNSS observation data; the satellite-borne observation data comprises satellite-borne GNSS observation data of a LEO satellite; a first model construction module, configured to construct BDS-3 and LNSS inter-satellite ranging observation models based on a dynamic orbit determination method according to the inter-satellite ranging observation data; a first determination module, configured to determine observation parameters of the BDS-3 and LNSS inter-satellite ranging observation models based on a Lagrange interpolation algorithm and a quadratic polynomial method; the observation parameters comprise signal transmission time estimation values, satellite dynamic parameters and satellite clock errors; wherein the quadratic polynomial method adopts a quadratic term model to simulate satellite clock error changes within an arc length; For satellites In one arc segment The satellite clock error at the time is represented as: ； wherein, , , respectively represent the parameters to be solved of the quadratic model, represents the starting time of the arc segment; is the time when the satellite transmits the signal; is the satellite clock error of the satellite ; a second construction module, configured to construct ground monitoring station GNSS and LNSS observation models according to the ground monitoring station observation data; the ground monitoring station GNSS and LNSS observation models comprise pseudo-range observation equations and carrier phase observation equations; a second determination module, configured to determine observation parameters of the ground monitoring station GNSS and LNSS observation models based on a Lagrange interpolation algorithm and an orbit integral function; a third construction module, configured to determine satellite-borne GNSS observation equations according to the satellite-borne observation data, and to determine a LEO satellite satellite-borne GNSS observation model according to the satellite-borne GNSS observation equations; a third determination module, configured to determine observation parameters of the LEO satellite satellite-borne GNSS observation model based on an orbit integral function and a Lagrange interpolation algorithm; a model construction module, configured to construct a GNSS and LNSS multi-source data fusion observation model according to multi-source parameters by adopting a quadratic polynomial modeling and Lagrange polynomial interpolation method; the multi-source parameters comprise observation parameters of the BDS-3 and LNSS inter-satellite ranging observation models, observation parameters of the ground monitoring station GNSS and LNSS observation models, and observation parameters of the LEO satellite satellite-borne GNSS observation model; a solution module, configured to perform parameter estimation on the GNSS and LNSS multi-source data fusion observation model by adopting a least square method to obtain a parameter solution result; the parameter solution result is used to represent positioning, navigation and timing of a satellite system.

8. An electronic device, comprising: The electronic device comprises a memory and a processor; the memory is used to store a computer program; the processor runs the computer program to enable the electronic device to perform the GNSS and LNSS data fusion processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to implement the GNSS and LNSS data fusion processing method according to any one of claims 1 to 6.

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