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CN117826200A - PPP-B2B-based marine real-time precise positioning method, system and medium - Google Patents

PPP-B2B-based marine real-time precise positioning method, system and medium Download PDF

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Publication number
CN117826200A
CN117826200A CN202410032551.XA CN202410032551A CN117826200A CN 117826200 A CN117826200 A CN 117826200A CN 202410032551 A CN202410032551 A CN 202410032551A CN 117826200 A CN117826200 A CN 117826200A
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ppp
differential
correction
satellite
representing
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单瑞
聂志喜
陆凯
王振杰
杨源
刘慧敏
秦轲
于得水
周庐艳
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China University of Petroleum East China
Qingdao Institute of Marine Geology
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China University of Petroleum East China
Qingdao Institute of Marine Geology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a PPP-B2B-based marine real-time precise positioning method, a system and a medium, belonging to the field of information technology service; firstly, the PPP-B2B correction broadcasted by the Beidou No. three GEO satellite is used, broadcast ephemeris data are combined, and precision clock error and orbit based on the PPP-B2B correction are recovered; then obtaining GNSS observation data, correspondingly constructing a non-differential ionosphere combined observation model and an epoch differential ionosphere combined observation model for satellites with PPP-B2B correction and without PPP-B2B correction, applying PPP-B2B precise clock difference and orbit to the non-differential ionosphere combined observation model, constructing a signal-to-noise ratio random model and calculating satellite weight; and finally, carrying out time updating based on the epoch differential ionosphere combined observation model, carrying out measurement updating based on the non-differential ionosphere combined observation model, and carrying out parameter estimation by adopting a mixed differential single-station Kalman filter to finish the offshore real-time positioning.

Description

PPP-B2B-based marine real-time precise positioning method, system and medium
Technical Field
The invention belongs to the technical field of information technology service, and particularly relates to a method, a system and a medium for real-time precision positioning on the sea based on PPP-B2B.
Background
Along with the rapid development of GNSS (Global Navigation Satellite System), the GNSS high-precision positioning technology is widely applied to the fields of maritime search and rescue, marine exploration and the like, and the obtained high-precision positioning information is a basic premise of all marine development activities. Currently, there are two main types of GNSS high-precision positioning techniques: dynamic differential positioning (RTK) techniques and precision single point positioning (Precise Point Positioning, PPP) techniques. The PPP method can obtain high-precision positioning information by only using a single receiver, and compared with RTK technology, PPP is easier to implement at sea. At present, RTS service of IGS (International GNSS Service, IGS) is commonly adopted to realize real-time PPP, but the broadcasting of RTS signals depends on a network, and is difficult to apply in the ocean.
Some commercial companies in the world have developed satellite broadcasting services to provide users with real-time and high-precision positioning results even in the case of difficult communication in the offshore network. Currently, commercial high-precision positioning services mainly comprise an OmniStar system, a Starfire system of NavCom company, an RTX system of Trimble company, a StarFix system of Fugro company and the like. However, such commercial services often require expensive royalties to be paid for authorization, which is disadvantageous for mass-oriented low-cost PPP high-precision positioning applications. BDS-3 has issued PPP enhancement service, namely PPP-B2B characteristic service, this service broadcasts the correction information through the geosynchronous orbit satellite in 2020, offer free, public high-accuracy positioning service for users in the peripheral area, and PPP-B2B service is not limited by network communication, hopefully realize the real-time high-accuracy positioning of ocean area low cost.
The invention patent with the application publication number of [ CN114280644A ] discloses a precision single point positioning system and a precision single point positioning method based on PPP-B2B service, wherein the precision single point positioning system and the precision single point positioning method firstly receive PPP-B2B signals broadcast by a Beidou No. three system, observe data and broadcast ephemeris, and decode and preprocess the received PPP-B2B signals to obtain a correction number; correcting the observed data according to the correction, and solving known items in the observed data through the ionosphere-free model; finally, outputting a corresponding precise single-point positioning result according to the user requirement; the invention patent with application publication number of [ CN115616615A ] discloses a PPP-B2B enhanced low-cost single-frequency GNSS receiver precise positioning method, which is characterized in that navigation signals from GPS/BDS-3 satellites are received through ground equipment, broadcast ephemeris is corrected through PPP-B2B real-time enhancement correction played by Beidou three-number GEO satellites, and real-time single-frequency precise single-point positioning is completed in the ground equipment by adopting a PPP-B2B correction real-time single-frequency precise single-point positioning algorithm model.
However, in the offshore environment, large ships and water surfaces will generate larger reflection signals, specular reflection formed by the smooth surfaces will generate larger multipath errors than diffuse reflection formed by the rough surfaces, and further the positioning precision of the PPP-B2B technology on the sea is reduced, so that the current PPP-B2B technology is not applied to real-time precise positioning on the sea.
Disclosure of Invention
Aiming at the problem that the PPP-B2B service is low in offshore positioning precision and difficult to apply, the invention provides a real-time accurate positioning method, a system and a medium based on PPP-B2B, which are used for performing PPP-B2B positioning by adopting a mixed differential single-station Kalman filter after satellite weight is calculated by using a signal-to-noise ratio random model, thereby improving the positioning precision of the offshore PPP-B2B and providing a stable, reliable and low-cost real-time positioning method for offshore environment users without network communication.
The invention is realized by adopting the following technical scheme: a real-time precise marine positioning method based on PPP-B2B comprises the following steps:
step A, combining broadcast ephemeris to perform precision ephemeris calculation based on Beidou No. three PPP-B2B service, wherein the calculation mainly comprises the calculation of precision orbit and clock error and comprises the following steps:
1) Expressing the precise orbit correction under the satellite orbit coordinate system as the precise orbit correction under the geocentric earth fixed coordinate system:
wherein [ delta O r δO a δO c ] T Is the orbit correction under Radial, tangential and normal star-fixed (ACR) coordinate systems; [ delta ] O x δO y δO z ] T -orbit correction for the satellite under the geocentric earth fixed (Earth Center Earth Fixed, ECEF) reference frame;
wherein r andfor satellite position and satellite velocity calculated using broadcast ephemeris.
2) The precision orbit based on PPP-B2B service is calculated:
wherein,representing the precise coordinates of the satellite in the geocentric earth fixed (Earth Center Earth Fixed, ECEF) reference frame recovered by the PPP-B2B correction; />Representing the coordinates of the satellite under the geocentric fixed reference frame calculated from the broadcast ephemeris.
3) Calculating precision clock difference based on PPP-B2B service by combining satellite clock difference provided by broadcast ephemeris
Wherein,representing the precision clock error recovered by the PPP-B2B correction, < >>Representing satellite clock differences calculated from broadcast ephemeris, C 0 The clock correction parameter obtained in the PPP-B2B message is shown, and c is the speed of light.
Step B, acquiring GNSS observation data, carrying out data preprocessing and error correction, respectively constructing a non-differential ionosphere combined observation model and an epoch differential ionosphere combined observation model corresponding to a satellite with PPP-B2B correction and a satellite without PPP-B2B correction, applying PPP-B2B precise clock and orbit to the non-differential ionosphere combined observation model, and then constructing a signal-to-noise ratio random model to calculate satellite weights, wherein the method specifically comprises the following steps of:
1) The data preprocessing mainly comprises cycle slip detection and repair and gross error elimination, wherein an ionosphere residual error method is adopted to detect cycle slip, and MW combined residual error is adopted to detect gross error; the error correction mainly comprises tropospheric delay, relativistic effect correction, phase winding, earth rotation correction, tidal effect correction and the like, and is carried out by adopting the existing model.
2) Calculating satellite precise position and clock error by using PPP-B2B correction product, calculating a non-differential ionosphere combined pseudo-range observation value based on an original pseudo-range observation value in GNSS observation data, and calculating a non-differential ionosphere combined phase observation value based on an original phase observation value, thereby further obtaining a linearized non-differential ionosphere combined observation model:
wherein, the superscript G and C in each symbol respectively represent GPS and BDS-3 systems, the superscript s represents satellite end, the subscript r represents receiver end, and the subscript IF represents ionosphere combination;and->The non-differential ionosphere combination pseudo-range and the phase observation value of the GPS satellite are respectively represented by subtracting the satellite distance and each error correction; />The cosine vector, the satellite distance and +.>Obtaining by the receiver approximate coordinates and the recovered precision orbit of PPP-B2B; δr e An increment representing a more approximate coordinate of the receiver's position in the geocentric fixed coordinate system; />Representing receiver clock skew; m is m w Representing a tropospheric wet delay projection function, and adopting a global projection function; δT w Is the tropospheric wet delay in zenith direction; lambda (lambda) IF Representing the ionosphere combined wavelength; />Combining ambiguity for the ionosphere; delta ISB G-C Is an intersystem deviation parameter; />And->The pseudo-range and phase observation noise, which respectively represent non-differential ionosphere combinations, are estimated as zero-mean white noise.
In addition, the unspecified satellite clock error is obtained from the precise clock error recovered from PPP-B2B, and errors such as relativistic effects, sagnac effects, shape delays, phase wrapping and tidal effects have been corrected by the existing model. The unknown parameters in the above equation include the receiver coordinate increment δr e Clock error of GPS system receiverZenithal troposphere wet delay δT w Inter-system bias parameter δisb G-C Ambiguity +.>And->Estimating parameters to be estimated in a non-difference ionosphere combined observation model:
wherein x represents a parameter vector to be estimated of the non-differential ionospheric combined observation model, and if n GPS satellites and m BDS-3 satellites are observed simultaneously in the current epoch, the complete non-differential ionospheric combined observation model is expressed as:
wherein p is s And l s Respectively representing n GPS satellites and m BDS-3 satellitesAnd->A vector of components; h represents a coefficient matrix of the non-differential ionosphere combined observation model; />And->Respectively representing n GPS satellites and m BDS-3 satellitesAnd->The component vectors, expressed as:
for satellites without PPP-B2B correction, calculating an epoch differential ionosphere combined phase observation value by using an original phase observation value, and constructing an epoch differential ionosphere combined observation model by using broadcast ephemeris, wherein the method comprises the following steps of:
wherein,subtracting each error correction from the epoch differential ionosphere combined phase observation; Δρ k,k-1 The satellite positions required for calculating the station star distances are obtained from broadcast ephemeris in the epoch differential ionosphere combined observation model; />Receiver clock difference variation values representing front and rear epochs; />Representing epoch differential ionosphere combined phase observation noise; under the condition that cycle slip does not occur, the ambiguity change value is 0, and the inter-system deviation parameter and the zenith troposphere wet delay have strong time correlation and are eliminated through epoch difference.
Parameters to be estimated of the epoch differential ionosphere combination observation model include receiver adjacent epoch displacement and receiver clock difference change value:
wherein dz k,k-1 Parameter vector dr to be estimated representing epoch differential ionosphere combined observation model k,k-1 Indicating the position change of the front and back epoch of the receiver, and j satellites without PPP-B2B correction are arranged, and then the complete epoch differential ionosphere combination observation model is expressed as follows:
wherein DeltaL s Representing j satellitesA vector of components; />A coefficient matrix representing an epoch differential ionosphere combined observation model; />Representing +.>A component vector.
3) Because the signal quality of each satellite is different due to the influence of factors such as the position of each satellite, a reasonable random model is needed to weight each satellite, and whether the model is accurate or not directly influences the PPP-B2B positioning precision, so that an accurate random model is also necessary when PPP-B2B data processing is carried out. Significant multipath effects at sea will seriously affect the accuracy of PPP-B2B positioning. The signal-to-noise ratio of the satellite signal has correlation with multipath, so the scheme constructs a signal-to-noise ratio random model, reduces the influence of multipath effect on PPP-B2B positioning accuracy, and the constructed signal-to-noise ratio random model is as follows:
in the method, in the process of the invention,and->Noise variances respectively representing the original pseudo-range observations and the original phase observations; c (C) p And C l For model coefficients, C/N 0 s Signal to noise ratio for satellite s;
furthermore, the noise variance applied to the measured and updated non-differential ionosphere combined pseudorange and phase observations is:
wherein,
wherein, subscripts i and j in each symbol respectively represent two frequencies participating in the ionosphere combination;and->Noise variances of satellite s non-differential ionosphere combined pseudoranges and phase observations are represented respectively;f i and f j The magnitudes of frequency i and frequency j are represented, respectively; />Representing the noise variance of the PPP-B2B precision product; URA represents the user ranging accuracy factor advertised by the PPP-B2B product.
The noise variance of the epoch differential ionosphere combined phase observations applied to the time update is:
wherein,representing the noise variance of the satellite s epoch differential ionosphere combined phase observations.
Step C, finally, according to the signal-to-noise ratio random model, carrying out time updating based on the epoch difference ionosphere combination observation model, carrying out measurement updating based on the non-difference ionosphere combination observation model, and carrying out parameter estimation by adopting mixed difference single-station Kalman filtering to finish offshore real-time positioning;
1) For satellites without Beidou PPP-B2B corrections, calculating the displacement of a receiver relative to the previous epoch based on an epoch differential ionosphere combination observation model, updating time, for satellites with Beidou PPP-B2B corrections, adopting a non-differential ionosphere combination observation model to update measurement, and finally adopting a mixed differential single-station Kalman filtering to complete parameter estimation.
The Kalman filtering is divided into two steps, namely time update and measurement update:
the formula for updating time based on the epoch difference ionosphere combination observation model and the signal to noise ratio random model is as follows:
wherein,the state parameter estimates and the covariance matrix at epoch k-1 are respectively represented. />Andrespectively representing state parameter predicted values at epoch k time and corresponding covariance matrix. dx (dx) k,k-1 、/>Representing the state change and covariance matrix thereof. The state variable is composed of adjacent epoch displacement of receiver, clock difference variation value of receiver and intersystem offsetThe difference parameter variation value and the troposphere zenith humidity delay variation value and the ambiguity variation value.
Wherein the receiver adjacent epoch displacement and the receiver clock difference change value, the least squares estimated parameter dz is obtained from the epoch differential ionosphere combined observation model k,k-1 The method comprises the steps that a troposphere zenith wet delay change value is determined according to troposphere dynamic noise, and when cycle slip does not occur, an intersystem deviation parameter and ambiguity are used as constants to estimate;
and carrying out measurement updating based on the non-difference ionosphere combined observation model and the signal-to-noise ratio random model:
y k ＝H k ·x k +∈ k ，∈ k ～N(0,R k )
wherein y is k To observe the vector, H k Coefficient matrix, x, of non-differential ionosphere combined model k Represent state vector, E k Observing noise vector for non-difference ionosphere combination, R k For observing noise vector epsilon k Is the covariance matrix of G k Is a gain matrix that is used to determine the gain of the gain element,and->The parameter estimation value and the covariance matrix after the update are measured.
2) After carrying out adjustment on parameters to be estimated by adopting mixed differential single-station Kalman filtering, obtaining a receiver coordinate increment, a GPS system receiver clock error, zenith troposphere wet delay, intersystem deviation parameters and ambiguity parameters, then carrying out quality control on adjustment results, searching maximum positioning residual errors for quality analysis, if the maximum residual errors are larger than a certain threshold value, considering that rough differences exist, removing corresponding satellites, and then carrying out re-filtering until all the residual errors are smaller than the threshold value; and finally, adding the approximate coordinates to the receiver coordinate increment obtained after the quality control to obtain the receiver coordinates and outputting the receiver coordinates to realize real-time precise single-point positioning service for offshore users.
The invention further provides a positioning system of the marine real-time precise positioning method based on PPP-B2B, wherein the positioning system comprises a PPP resolving module and a PPP-B2B decoding module;
the PPP-B2B decoding module is used for combining broadcast ephemeris data, realizing PPP-B2B decoding and IOD matching, and obtaining a PPP-B2B real-time precise track and a clock correction;
the PPP resolving module is used for preprocessing GNSS observation data and correcting errors, constructing a non-differential ionosphere combined observation model, an epoch differential ionosphere combined observation model and a random model corresponding to the model, applying a real-time precise orbit and a clock correction, carrying out measurement updating based on the non-differential ionosphere combined observation model, carrying out time updating based on the epoch differential ionosphere combined observation model, and adopting mixed differential single-station Kalman filtering to complete parameter estimation so as to realize real-time offshore positioning resolving.
The invention further proposes a computer-readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the real-time precision positioning method on the sea based on PPP-B2B.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the scheme, the PPP-B2B correction broadcasted by the Beidou No. three GEO satellite is obtained, and the precision clock error and orbit based on the PPP-B2B correction are recovered by combining broadcast ephemeris; after the GNSS observation data are acquired, the precise clock error and orbit based on the PPP-B2B correction are applied to construct a non-differential ionosphere combination observation model and an epoch differential ionosphere combination observation model for the satellites with the PPP-B2B correction and the satellites without the PPP-B2B correction respectively, and a signal to noise ratio random model is constructed to calculate satellite weights; time updating is carried out based on the epoch difference ionosphere combination observation model and the signal-to-noise ratio random model, measurement updating is carried out based on the non-difference ionosphere combination observation model and the signal-to-noise ratio random model, parameter estimation is carried out by adopting mixed difference single-station Kalman filtering, and real-time high-precision offshore positioning is completed;
the method has the advantages that the precision ephemeris and the precision clock error are recovered by utilizing the orbit and the clock error correction broadcast by the PPP-B2B service and combining the broadcast ephemeris, and the marine real-time high-precision positioning is performed by adopting a signal-to-noise ratio random model and a mixed differential single-station Kalman filter, so that the problem that the marine PPP-B2B positioning precision is low and difficult to apply in real time is solved; the method can provide accurate and reliable offshore real-time positioning service, can be applied to the fields of offshore emergency search and rescue centers, offshore energy exploration and the like in the future, and can generate remarkable economic benefits.
Drawings
FIG. 1 is a schematic flow chart of a real-time offshore precise positioning method according to an embodiment of the invention;
FIG. 2 is a diagram showing a comparison of PPP-B2B positioning error and a positioning error using a commercial star station differential technique in an offshore positioning experiment according to an embodiment of the present invention;
fig. 3 is a diagram showing a comparison of PPP-B2B positioning convergence time and commercial star station differential positioning convergence time in an offshore positioning experiment according to an embodiment of the invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be more readily understood, a further description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the present invention is not limited to the specific embodiments disclosed below.
Embodiment 1, this embodiment proposes a real-time precision positioning method on the sea based on PPP-B2B, comprising the following steps:
step A, acquiring a PPP-B2B correction broadcasted by a Beidou No. three GEO satellite, combining broadcast ephemeris data, and recovering a precision clock error and an orbit based on the PPP-B2B correction;
in order to ensure the relevance between information contents broadcasted by different information types and the relevance between correction information and broadcast ephemeris, the information is identified through four version numbers of a space state expression data option number (IOD SSR), a pseudo-random noise code data option number (IODP), a navigation data option number (IODN) and a correction data identification and data option number (IOD Corr), so that the information is convenient to match and use;
(1) The epoch time interval of the track correction in the track correction of the PPP-B2B message is 48s, and the broadcast track correction information comprises a track correction vector. The satellite position correction vector can be calculated using the orbit correction values. After the IODN of the orbit correction information is successfully matched with the IODC in the broadcast ephemeris navigation message, the precise orbit correction under the satellite orbit coordinate system can be expressed as the precise orbit correction under the geocentric earth fixed coordinate system:
wherein [ delta O r δO a δO v ] T Is the orbit correction under Radial, tangential and normal star-fixed (ACR) coordinate systems; [ delta ] O x δO y δO z ] T Is the orbit correction of the satellite in the geocentric earth fixed (Earth Center Earth Fixed, ECEF) reference frame. [ e ] r e a e c ]The unit vectors corresponding to radial, tangential and normal, respectively, can be expressed as:
wherein r andsatellite position calculated for use with broadcast ephemerisSetting and satellite speed;
the precision orbit based on PPP-B2B service is calculated:
wherein the method comprises the steps ofRepresenting the precise coordinates of the satellite in the geocentric earth fixed (Earth Center Earth Fixed, ECEF) reference frame recovered by the PPP-B2B correction; />Representing the coordinates of the satellite under the geocentric fixed reference frame calculated from the broadcast ephemeris.
(2) The epoch time interval of the clock correction in the PPP-B2B clock correction information is 6s; after the clock correction information is successfully matched with the broadcast ephemeris, the clock correction parameter C contained in the telegraph text can be utilized 0 Correcting the clock error parameters obtained by calculating the broadcast ephemeris. The precision clock skew based on PPP-B2B services is calculated:
wherein,representing the precision clock error recovered by the PPP-B2B correction, < >>Representing satellite clock differences calculated from broadcast ephemeris, C 0 Indicating the clock correction parameter obtained in the PPP-B2B message, c indicating the speed of light; when the orbit correction and the clock correction information of the PPP-B2B message are the same and the IOD Corr in the clock correction information can be matched with the IODC in the navigation message, the orbit correction and the clock correction can be matched for use, and error correction is carried out.
B, acquiring GNSS observation data, performing data preprocessing and error correction, respectively constructing a non-differential ionosphere combined observation model and an epoch differential ionosphere combined observation model corresponding to a satellite with PPP-B2B correction and a satellite without PPP-B2B correction, applying the PPP-B2B precise clock difference and orbit calculated in the step A to the non-differential ionosphere combined observation model, and then constructing a signal-to-noise ratio random model to calculate satellite weights;
(1) The data preprocessing mainly comprises cycle slip detection and repair and gross error elimination. Detecting cycle slip by adopting an ionosphere residual error method, and detecting a gross error by adopting MW combined residual error; the error correction mainly comprises tropospheric delay, relativistic effect correction, phase winding, earth rotation correction, tidal effect correction and the like, and is corrected by adopting the existing model, and is not described in detail herein.
(2) In the non-differential ionosphere combined observation model, the satellite precision position and the clock difference are calculated by applying PPP-B2B correction products, a non-differential ionosphere combined pseudo-range observation value is calculated based on an original pseudo-range observation value in GNSS observation data, a non-differential ionosphere combined phase observation value is calculated based on an original phase observation value, and a linearized non-differential ionosphere combined observation model is further obtained:
wherein, the superscript G and C in each symbol respectively represent GPS and BDS-3 systems, the superscript s represents satellite end, the subscript r represents receiver end, and the subscript IF represents ionosphere combination;and->The non-differential ionosphere combination pseudo-range and the phase observation value of the GPS satellite are respectively represented by subtracting the satellite distance and each error correction; />The cosine vector, the satellite distance and +.>Obtaining by the receiver approximate coordinates and the recovered precision orbit of PPP-B2B; δr e An increment representing a more approximate coordinate of the receiver's position in the geocentric fixed coordinate system; />Representing receiver clock skew; m is m w Representing a tropospheric wet delay projection function, and adopting a global projection function; δT w Is the tropospheric wet delay in zenith direction; lambda (lambda) IF Representing the ionosphere combined wavelength; />Combining ambiguity for the ionosphere; delta ISB G-C Is an intersystem deviation parameter; />And->The pseudo-range and phase observation noise, which respectively represent non-differential ionosphere combinations, are estimated as zero-mean white noise.
In addition, the unspecified satellite clock error is obtained from the precise clock error recovered from PPP-B2B, and errors such as relativistic effects, sagnac effects, shape delays, phase wrapping and tidal effects have been corrected by the existing model. Unknown parameters in the above equation, including receiver coordinate increment δr e Clock error of GPS system receiverZenithal troposphere wet delay δT w Inter-system bias parameter δisb G-C Ambiguity +.>And->Estimating parameters to be estimated in a non-difference ionosphere combined observation model:
where x represents the vector of parameters to be estimated of the non-differential ionosphere combined observation model. If n GPS satellites and m BDS-3 satellites are observed at the same time in the current epoch, the complete non-differential ionosphere combined observation model is expressed as follows:
wherein p is s And l s Respectively representing n GPS satellites and m BDS-3 satellitesAnd->A vector of components; h represents a coefficient matrix of the non-differential ionosphere combined observation model; />And->Respectively representing n GPS satellites and m BDS-3 satellitesAnd->The component vectors, expressed as:
because the current PPP-B2B service only broadcasts satellite corrections of GPS and BDS-3 systems, satellites of Galileo, GLONASS and other systems lack PPP-B2B corrections, so that precise clock differences and precise ephemeris cannot be obtained, for the satellites without PPP-B2B corrections, an epoch differential ionosphere combination phase observation value is calculated by using an original phase observation value, and an epoch differential ionosphere combination observation model is constructed by using broadcast ephemeris, specifically:
wherein,subtracting each error correction from the epoch differential ionosphere combined phase observation; Δρ k,k-1 The satellite positions required for calculating the station star distances are obtained from broadcast ephemeris in the epoch differential ionosphere combined observation model; />Receiver clock difference variation values representing front and rear epochs; />Representing epoch differential ionosphere combined phase observation noise; under the condition that cycle slip does not occur, the combined phase observation value of the epoch difference ionosphere has an ambiguity change value of 0, and the inter-system deviation parameter and the zenith troposphere wet delayHas strong time correlation and is eliminated by epoch difference.
Parameters to be estimated of the epoch differential ionosphere combination observation model include receiver adjacent epoch displacement and receiver clock difference change value:
wherein dz k,k-1 Parameter vector dr to be estimated representing epoch differential ionosphere combined observation model k,k-1 Indicating the amount of receiver front and back epoch position change. J satellites without PPP-B2B corrections are arranged, and then the complete epoch differential ionosphere combined observation model is expressed as follows:
wherein DeltaL s Representing j satellitesA vector of components; />A coefficient matrix representing an epoch differential ionosphere combined observation model; />Representing +.>A component vector.
(3) Because the signal quality of each satellite is different due to the influence of factors such as the position of each satellite, a reasonable random model is needed to weight each satellite, and whether the model is accurate or not directly influences the PPP-B2B positioning precision, so that an accurate random model is also necessary when PPP-B2B data processing is carried out. Significant multipath effects at sea will seriously affect the accuracy of PPP-B2B positioning. The signal-to-noise ratio of the satellite signal has correlation with multipath, so the scheme constructs a signal-to-noise ratio random model, reduces the influence of multipath effect on PPP-B2B positioning accuracy, and the constructed signal-to-noise ratio random model is as follows:
in the method, in the process of the invention,and->Noise variances respectively representing the original pseudo-range observations and the original phase observations; c (C) p And C l For model coefficients, C/N 0 s Signal to noise ratio for satellite s;
furthermore, the noise variance applied to the measured and updated non-differential ionosphere combined pseudorange and phase observations is:
wherein,
wherein, subscripts i and j in each symbol respectively represent two frequencies participating in the ionosphere combination;and->Noise variances of satellite s non-differential ionosphere combined pseudoranges and phase observations are represented respectively;f i and f j The magnitudes of frequency i and frequency j are represented, respectively; />Representing the noise variance of the PPP-B2B precision product; URA represents the user ranging accuracy factor advertised by the PPP-B2B product.
The noise variance of the epoch differential ionosphere combined phase observations applied to the time update is:
wherein,representing the noise variance of the satellite s epoch differential ionosphere combined phase observations.
And C, carrying out measurement updating based on the non-differential ionosphere combined observation model and the signal-to-noise ratio random model, carrying out time updating based on the epoch differential ionosphere combined observation model and the signal-to-noise ratio random model, and carrying out parameter estimation by adopting a mixed differential single-station Kalman filter to finish offshore real-time positioning. .
(1) For satellites without Beidou PPP-B2B correction, calculating the displacement relative to the previous epoch based on an epoch differential ionosphere combination observation model, and carrying out time update; and for the satellite with the Beidou PPP-B2B correction, adopting a non-differential ionosphere combined observation model to update measurement, and finally adopting a mixed differential single-station Kalman filter to complete parameter estimation.
The Kalman filtering is divided into two steps, namely, time update and measurement update.
The formula for updating time based on the epoch difference ionosphere combination observation model and the signal to noise ratio random model is as follows:
wherein,the state parameter estimates and the covariance matrix at epoch k-1 are respectively represented. />And->Respectively representing state parameter predicted values at epoch k time and corresponding covariance matrix. dx (dx) k,k-1 、/>Representing the state change and covariance matrix thereof. The state variable consists of adjacent epoch displacement of the receiver, a clock difference variable value of the receiver, a zenith moisture delay variable value of the troposphere, a systematic deviation parameter variable value and an ambiguity variable value.
Wherein the receiver adjacent epoch displacement and the receiver clock difference change value, the least squares estimated parameter dz is obtained from the epoch differential ionosphere combined observation model k,k-1 The method comprises the steps that a troposphere zenith wet delay change value is determined according to troposphere dynamic noise, and when cycle slip does not occur, an intersystem deviation parameter and ambiguity are used as constants to estimate;
measurement updating based on non-differential ionosphere combined observation model and signal-to-noise ratio random model
y k ＝H k ·x k +∈ k ，∈ k ～N(0,R k )
Wherein y is k To observe the vector, H k Coefficient matrix, x, of non-differential ionosphere combined model k Represent state vector, E k Observing noise vector for non-difference ionosphere combination, R k For observing noise vector epsilon k Is the covariance matrix of G k Is a gain matrix that is used to determine the gain of the gain element,and->The parameter estimation value and the covariance matrix after the update are measured.
(2) After carrying out adjustment on parameters to be estimated by adopting mixed differential single-station Kalman filtering, obtaining a receiver coordinate increment, a GPS system receiver clock error, zenith troposphere wet delay, intersystem deviation parameters and ambiguity parameters, then carrying out quality control on adjustment results, searching maximum positioning residual errors for quality analysis, if the maximum residual errors are larger than a certain threshold value, considering that rough differences exist, removing corresponding satellites, and then carrying out re-filtering until all the residual errors are smaller than the threshold value; and finally, adding the approximate coordinates to the receiver coordinate increment obtained after the quality control to obtain the receiver coordinates and outputting the receiver coordinates to realize real-time precise single-point positioning service for offshore users.
The precise positioning method disclosed by the embodiment utilizes the track, clock error and code deviation correction value broadcast by the PPP-B2B service to acquire the offshore high-precision positioning information in real time, solves the problem that the offshore PPP-B2B positioning accuracy is low and is difficult to apply in real time, provides a stable, reliable and low-cost real-time high-precision positioning method for users in an offshore network-free communication environment, and provides technical support for the fields of offshore rescue, ocean energy exploration and the like.
An embodiment 2, based on the positioning method of embodiment 1, proposes a positioning system of a real-time precision positioning method on the sea based on PPP-B2B, the positioning system including a PPP resolving module and a PPP-B2B decoding module;
with reference to fig. 1, the PPP-B2B decoding module is configured to combine broadcast ephemeris data, implement PPP-B2B decoding and IOD matching, and obtain a PPP-B2B real-time track and a clock correction; the PPP resolving module is used for preprocessing GNSS observation data and correcting errors, then respectively constructing a non-differential ionosphere combined observation model and an epoch differential ionosphere combined observation model for satellites with PPP-B2B corrections and without PPP-B2B corrections, and constructing a signal to noise ratio random model to calculate satellite weights; and performing time updating based on the epoch difference ionosphere combination observation model and the signal-to-noise ratio random model, performing measurement updating based on the non-difference ionosphere combination observation model and the signal-to-noise ratio random model, and performing parameter estimation by adopting mixed difference single-station Kalman filtering to realize real-time accurate positioning at sea.
Embodiment 3, based on the positioning method set forth in embodiment 1, the present embodiment provides a computer device and a computer readable storage medium, in which a computer program is stored, where the computer device includes a memory and a processor, and the memory stores the computer program, where the computer program, when executed by the processor, causes the processor to execute the steps of the PPP-B2B based real-time precise positioning method set forth in embodiment 1.
The application scenario of the real-time precision positioning method on the sea based on PPP-B2B is illustrated as follows:
in order to evaluate the performance of the offshore real-time precise positioning method based on PPP-B2B, the positioning results of the commercial real-time star station differential positioning technology are adopted for comparison.
The marine GNSS positioning experiment time is 2023, 3, 6 (yearly long and 65) 00:40:00 to 5:40:00 (in GPS), the place is the Bay area of Qingdao Jiaozhou in Shandong. And respectively acquiring PPP-B2B telegraph text by using an FRLL-PlUS receiver, acquiring a GNSS observation value of the mobile station by using a NovAtel Pwrpark7D receiver, and acquiring a satellite station differential positioning result by using a NavCom SF3050 receiver. Meanwhile, a reference station is erected on the shore, a span M300 receiver is adopted to obtain a GNSS observation value of the reference station, and an RTK (real time kinematic) calculation result is used as a reference position by using Inertial Explorer (IE 8.90) software. The acquired GNSS observations comprise the original pseudo-range and phase observations of GPS, GLONASS, galileo, BDS and BDS3 system double frequencies, and the sampling rate is 1s. And (3) evaluating the satellite station difference, namely the positioning precision and the positioning convergence speed of Starfire and PPP-B2B, wherein the positioning convergence judgment conditions are that the positioning error in the east and north directions is less than 30cm, the positioning error in the vertical direction is less than 60cm, and the time is kept for more than 1 minute. Because of a certain convergence time of the two positioning methods, the positioning accuracy is evaluated according to the result 1 hour after the experiment is selected and started, and the evaluation time is 4 hours.
Table 1 positioning accuracy statistics table for marine positioning experiment
Table 1 shows Bias and RMS statistics for the positioning error sequences in four dimensions, north-south, east-west, vertical and 3D for the star station differential and PPP-B2B positioning methods. In addition, fig. 2 and 3 show the satellite station differential and PPP-B2B positioning error sequences, respectively, and the convergence time of the two methods. It can be seen that by adopting a signal-to-noise ratio random model and a mixed differential single-station Kalman filter, the overall positioning precision and convergence time of PPP-B2B at sea are equivalent to those of a commercial star station differential positioning technology, a stable, reliable and low-cost real-time position calculation method can be provided for users in a marine non-network communication environment, and the method can be widely applied to the fields of marine energy exploration and the like.
The present invention is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present invention without departing from the technical content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims (9)

1. The marine real-time precise positioning method based on PPP-B2B is characterized by comprising the following steps of:
step A, acquiring a PPP-B2B correction broadcasted by a Beidou No. three system, combining broadcast ephemeris data, and recovering a precision clock error and a track based on the PPP-B2B correction;
step B, acquiring GNSS observation data, carrying out data preprocessing and error correction, respectively constructing a non-differential ionosphere combined observation model and an epoch differential ionosphere combined observation model corresponding to a satellite with PPP-B2B correction and a satellite without PPP-B2B correction, applying a precise clock difference and orbit based on PPP-B2B correction to the non-differential ionosphere combined observation model, and then constructing a signal-to-noise ratio random model to calculate satellite weight;
and C, carrying out measurement updating based on the non-differential ionosphere combined observation model and the signal-to-noise ratio random model, carrying out time updating based on the epoch differential ionosphere combined observation model and the signal-to-noise ratio random model, and carrying out parameter estimation by adopting a mixed differential single-station Kalman filter to finish offshore real-time positioning.
2. The real-time precise marine positioning method based on PPP-B2B according to claim 1, wherein in the step B, the non-differential ionosphere combined observation model construction process is as follows:
for the satellite with PPP-B2B correction, calculating the satellite precision position and clock error by applying PPP-B2B correction product, calculating a non-differential ionosphere combined pseudo-range observation value based on an original pseudo-range observation value in GNSS observation data, and calculating a non-differential ionosphere combined phase observation value based on an original phase observation value, so as to further obtain a linearized non-differential ionosphere combined observation model:
wherein, the superscript G and C in each symbol respectively represent GPS and BDS-3 systems, the superscript s represents satellite end, and the subscript r represents satellite endAt the receiver end, the subscript IF represents the ionosphere combination;non-differential ionosphere combined pseudorange observations representing GPS satellites minus station-to-satellite distances and various error corrections,/->Subtracting the satellite distance and various error corrections from the non-differential ionosphere combined phase observations of the GPS satellites; />A direction cosine vector representing the direction from the user terminal to the satellite terminal; δr e An increment representing a more approximate coordinate of the receiver's position in the geocentric fixed coordinate system; />Representing receiver clock skew; m is m w Representing a tropospheric wet delay projection function; δT w Is the tropospheric wet delay in zenith direction; lambda (lambda) IF Representing the ionosphere combined wavelength; />Combining ambiguity for the ionosphere; delta ISB G-C Is an intersystem deviation parameter; />And->Respectively representing non-differential ionosphere combined pseudo-range and phase observation noise;
the unknown parameters in the above equation include the receiver coordinate increment δr e Clock error of GPS system receiverZenithal troposphere wet-spreadingLate delta T w Inter-system bias parameter δisb G-C Ambiguity +.>And->Estimating parameters to be estimated in a non-difference ionosphere combined observation model:
wherein x represents a parameter vector to be estimated of the non-differential ionospheric combined observation model, and if n GPS satellites and m BDS-3 satellites are observed simultaneously in the current epoch, the complete non-differential ionospheric combined observation model is expressed as:
wherein p is s And l s Respectively representing n GPS satellites and m BDS-3 satellitesAnd->A vector of components; />And->Respectively representing n GPS satellites and m BDS-3 satellites +.>And->A vector of components; h represents the coefficient matrix of the non-differential ionosphere combined observation model.
3. The real-time precise positioning method of the sea based on the PPP-B2B according to claim 1, wherein in the step B, the construction process of the epoch differential ionosphere combined observation model is as follows:
for satellites without PPP-B2B corrections, an epoch differential ionosphere combination phase observation value is calculated by using an original phase observation value, and a broadcast ephemeris is used for constructing an epoch differential ionosphere combination observation model:
wherein,subtracting each error correction from the epoch differential ionosphere combined phase observation; Δρ k,k-1 Representing the difference between the station star distances of the front and rear epochs; />Receiver clock difference variation values representing front and rear epochs; />Representing epoch differential ionosphere combined phase observation noise;
parameters to be estimated of the epoch differential ionosphere combination observation model include receiver adjacent epoch displacement and receiver clock difference change value:
wherein dz k,k-1 Parameter vector dr to be estimated representing epoch differential ionosphere combined observation model k,k-1 Indicating the position change of the front and back epoch of the receiver, and j satellites without PPP-B2B correction are arranged, and then the complete epoch differential ionosphere combination observation model is expressed as follows:
wherein DeltaL s Representing j satellitesA vector of components; />A coefficient matrix representing an epoch differential ionosphere combined observation model; />Representing +.>A component vector.
4. The real-time precise positioning method of the sea based on PPP-B2B according to claim 1, wherein in the step B, a signal-to-noise ratio random model is built to calculate satellite weights, and the built signal-to-noise ratio random model is as follows:
in the method, in the process of the invention,and->Noise variances respectively representing the original pseudo-range observations and the original phase observations; c (C) p And C l For model coefficients, C/N 0 s Signal to noise ratio for satellite s;
furthermore, the noise variance applied to the measured and updated non-differential ionosphere combined pseudorange and phase observations is:
wherein, subscripts i and j in each symbol respectively represent two frequencies participating in the ionosphere combination;and->Noise variances of satellite s non-differential ionosphere combined pseudoranges and phase observations are represented respectively; />f i And f j The magnitudes of frequency i and frequency j are represented, respectively; />Represents the noise variance of the PPP-B2B precision product, URA represents the user ranging precision factor broadcasted by PPP-B2B products;
the noise variance of the epoch differential ionosphere combined phase observations applied to the time update is:
wherein,representing the noise variance of the satellite s epoch differential ionosphere combined phase observations.
5. The real-time precise marine positioning method based on PPP-B2B according to claim 1, wherein the step C is specifically implemented by the following steps:
(1) Based on the epoch difference ionosphere combination observation model and the signal to noise ratio random model, time updating is carried out:
wherein,state parameter estimation values and covariance matrix respectively representing epoch k-1 time, ++>And->State parameter predictive value and corresponding covariance matrix, dx respectively representing epoch k moment k,k-1 、/>Representing the state change amount and covariance matrix thereof;
(2) And carrying out measurement updating based on the non-difference ionosphere combined observation model and the signal-to-noise ratio random model:
y k ＝H k ·x k +∈ k ,∈ k ～N(0,R k )
wherein y is k To observe the vector, H k Coefficient matrix, x, of non-differential ionosphere combined model k Represent state vector, E k Observing noise vector for non-difference ionosphere combination, R k For observing noise vector epsilon k Is the covariance matrix of G k Is a gain matrix that is used to determine the gain of the gain element,and->The parameter estimation value and the covariance matrix after the update are measured.
6. The real-time precise positioning method based on PPP-B2B in sea according to claim 1, wherein in the step a, the following method is specifically adopted:
(1) Expressing the precise orbit correction under the satellite orbit coordinate system as the precise orbit correction under the geocentric earth fixed coordinate system:
wherein [ delta O r δO a δO c ] T Track corrections in radial, tangential and normal star coordinate systems; [ delta ] O x δO y δO z ] T The orbit correction of the satellite under the geocentric earth fixed reference frame is calculated; [ e ] r e a e c ]Unit vectors corresponding to radial, tangential and normal directions, respectively;
(2) The calculation is performed based on the precision orbit of PPP-B2B service:
wherein the method comprises the steps ofRepresenting the precise coordinates of the satellite under the geocentric fixed reference frame recovered by the PPP-B2B correction; />Representing coordinates of the satellite under the geocentric earth fixed reference frame calculated from the broadcast ephemeris;
(3) The precision clock difference based on PPP-B2B is calculated by combining the satellite clock difference provided by the broadcast ephemeris:
wherein,representing the precision clock error recovered by the PPP-B2B correction, < >>Representing satellite clock differences calculated from broadcast ephemeris, C 0 The clock correction parameter obtained in the PPP-B2B message is shown, and c is the speed of light.
7. The method for real-time precise positioning at sea based on PPP-B2B according to claim 1, wherein in the step C, after the adjustment is performed on the parameters to be estimated by adopting the mixed differential single-station kalman filter, a receiver coordinate increment, a GPS system receiver clock error, a zenithal tropospheric wet delay, an intersystem deviation parameter and an ambiguity parameter are obtained, then quality control is performed on the adjustment result, a maximum positioning residual error is searched for performing quality analysis until all the residual errors are smaller than a threshold value, and finally, an approximate coordinate is added to the receiver coordinate increment obtained after the quality control, thereby realizing real-time precise single-point positioning service for the offshore user.
8. Positioning system based on a real-time precision positioning method at sea based on PPP-B2B according to any of the claims 1-7, characterized in that it comprises a PPP-resolving module and a PPP-B2B decoding module;
the PPP-B2B decoding module is used for combining broadcast ephemeris data, realizing PPP-B2B decoding and IOD matching, and obtaining PPP-B2B real-time track and clock correction;
the PPP resolving module is used for preprocessing GNSS observation data and correcting errors, constructing a non-differential ionosphere combined observation model for satellites with PPP-B2B corrections, combining the constructed signal-to-noise ratio random model, applying real-time orbit and clock correction to conduct measurement updating, constructing an epoch differential ionosphere combined observation model for satellites without PPP-B2B corrections, combining the signal-to-noise ratio random model to conduct time updating, and adopting hybrid differential Kalman filtering parameter estimation to achieve real-time offshore positioning.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the PPP-B2B based real-time fine positioning method at sea as defined in claims 1-7.
CN202410032551.XA 2024-01-10 2024-01-10 PPP-B2B-based marine real-time precise positioning method, system and medium Pending CN117826200A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118330699A (en) * 2024-06-13 2024-07-12 中交华南勘察测绘科技有限公司 Offshore dredging multi-beam sounding adaptation system based on Beidou No. 3
CN118330694A (en) * 2024-06-17 2024-07-12 山东科技大学 GNSS (Global navigation satellite System) offshore positioning receiver and positioning method based on remote management
CN118566955A (en) * 2024-07-31 2024-08-30 中交华南勘察测绘科技有限公司 Error analysis method of real-time precise single-point positioning algorithm based on Beidou PPP-B2B service

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118330699A (en) * 2024-06-13 2024-07-12 中交华南勘察测绘科技有限公司 Offshore dredging multi-beam sounding adaptation system based on Beidou No. 3
CN118330694A (en) * 2024-06-17 2024-07-12 山东科技大学 GNSS (Global navigation satellite System) offshore positioning receiver and positioning method based on remote management
CN118566955A (en) * 2024-07-31 2024-08-30 中交华南勘察测绘科技有限公司 Error analysis method of real-time precise single-point positioning algorithm based on Beidou PPP-B2B service

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