WO2019062030A1 - Algorithme rtk de réseau de diffusion bds/gps fondé sur un réseau en étoile - Google Patents

Algorithme rtk de réseau de diffusion bds/gps fondé sur un réseau en étoile Download PDF

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WO2019062030A1
WO2019062030A1 PCT/CN2018/078492 CN2018078492W WO2019062030A1 WO 2019062030 A1 WO2019062030 A1 WO 2019062030A1 CN 2018078492 W CN2018078492 W CN 2018078492W WO 2019062030 A1 WO2019062030 A1 WO 2019062030A1
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difference
satellite
double
indicates
station
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PCT/CN2018/078492
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潘树国
王彦恒
张瑞成
尚睿
汪登辉
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东南大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry

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  • the invention relates to a star network based BDS/GPS broadcast network RTK algorithm, in particular to a star network based BDS compatible GPS broadcast mode and compatible with two-way communication network RTK (Real Time Kinematic, real-time dynamic positioning technology Implementation technology, belongs to the field of GNSS real-time high-precision fast positioning.
  • RTK Real Time Kinematic, real-time dynamic positioning technology Implementation technology
  • Network RTK technology has become one of the most widely used GNSS precision positioning technologies, and can provide users with multi-scale positioning services such as meter, decimeter and centimeter in real time [1-3] .
  • the mainstream network RTK technology can be divided into VRS (Virtual Reference Station) technology, MAC (Master Auxiliary Concept) technology and FKP (Flachen Korrektur Parameter). Technology [4] .
  • the user first uploads its own probability coordinates, and the CORS (Continuously Operating Reference System) central solution software (hereinafter referred to as the central software) generates a virtual reference station in the vicinity of the user according to the user probability position, the user and the user
  • the virtual reference station forms an ultra-short baseline for solution.
  • the disadvantages of this technology are: 1 requires two-way communication, increases data delay 2 When the user position changes greatly (more than 5km), the virtual reference station will change, the user needs to re-initialize; 3 the center software needs to be based on the user's approximate location Generate virtual reference station information for each user, limiting user capacity; 4 because the virtual information is used, the differential correction information cannot be tracked; 5 although the VRS technology itself does not limit the number of reference stations in the network element, the current The software is basically based on triangulation, lacking redundant information; 6 atmospheric error processing is only determined by the central software, so that users can not use better algorithms [4] ; 7 need to upload user's general location information, exposing the user's location, especially In some special areas, this method is not even desirable, such as the military field.
  • MAC technology supports single two-way communication.
  • two-way communication the user uploads his own approximate location information.
  • the central software selects the reference station closest to the user as the primary reference station according to the user's location, and broadcasts the differential correction information to the user; in one-way communication, The user needs to know the predefined network element in which he is located, and then obtain the corresponding differential correction information.
  • this technology still has the same shortcomings as the VRS technology.
  • the one-way communication although the shortcomings of the VRS technology are overcome, the user himself needs to know the network element in which he is located, which is for the user. It is difficult to implement, especially when users enter unfamiliar areas.
  • software using MAC technology uses non-difference algorithms for network solution, with many parameters and complex models, which makes the solution efficiency of network elements. Low, network initialization time (from boot to network RTK service available) long.
  • the central software uses non-difference algorithm to realize network solution, extracts non-difference error, and performs regional modeling on spatial correlation error, parameterizes non-difference space correlation error of mobile station, broadcasts by broadcast mode, and rover Real-time positioning based on these parameters and their location.
  • the disadvantages of this technology are as follows: 1 The central software uses the non-difference algorithm for network solution, with many parameters and complex models, which makes the settlement efficiency low and the network initialization time long; 2 The establishment of the spatial correlation error model is completed by the central software, which limits the user's use. A more optimized algorithm.
  • the present invention provides a BDS/GPS broadcast network RTK algorithm based on a star network, and is compatible with two-way communication, and supports differential correction data by ground equipment, aviation aircraft or satellite broadcasting.
  • the server uses the UDP protocol to broadcast the differential correction data of all the star network elements in the whole network in real time, and the users in the broadcast mode receive the data, and then perform network element selection, atmospheric interpolation and baseline solution.
  • the user who uses the two-way communication uploads its own approximate location through the Ntrip protocol or the TCP/IP protocol, and the server broadcasts the user's network element differential correction data to the user, and the user obtains the data and performs atmospheric interpolation and baseline solution.
  • the server broadcasts the differential correction number to the two-way communication user, the data broadcasted according to the broadcast mode is still uninterrupted.
  • the invention provides a star network-based BDS/GPS broadcast network RTK algorithm, which comprises the following specific steps:
  • Step 1 Obtain the base station coordinates from the database, use the Delaunay triangulation algorithm to generate the Delaunay triangulation, and generate a star-shaped network composed of several star network elements and control the whole region on the basis of the triangulation network, wherein each A star network element consists of a primary station and a number of secondary stations corresponding thereto;
  • Step 2 Obtain the base station data in real time, form a double-difference observation with the baseline in the Delaunay triangulation, construct a first Kalman filter, estimate the baseline double-difference ambiguity and the zenith tropospheric wet delay ZTD in real time, and then generate each Baseline double difference atmospheric error of the baseline;
  • Step 3 Assign the baseline double-difference atmospheric error generated in step 2 to the corresponding star network element baseline, and use the star-shaped network element as a unit to unify the reference star, and generate a single-element atmosphere between each baseline satellite station of the star-shaped network element. error;
  • Step 4 Using a star-shaped network element as a unit, encoding a single-difference atmospheric error, all base station coordinates, and a master station observation value of each baseline satellite station of the star network element, and the encoded data is performed by using a UDP protocol. Broadcast or use two-way communication for broadcast, in which UDP protocol broadcasts all data and then proceeds to step 5; when using two-way communication, firstly, the user uploads the approximate location information, and then the central software broadcasts only to the user according to the user's location through Ntrip protocol or TCP/IP. The data of the star network element where the user is located, proceeds to step 6;
  • Step 5 the user receives the data in real time and judges the network element where it is located, according to the differential data of the network element in which the network element is interpolated to obtain the single-difference atmospheric error between the station and the primary station, and corrects the observation value to the primary station, and proceeds to step 7;
  • Step 6 the user receives the data in real time, according to the differential data of the network element, the inter-station single difference atmospheric error between the station and the main station is interpolated, and corrected to the observation value of the main station, and proceeds to step 7;
  • step 7 the user constructs a double-difference observation value by using the corrected observation value of the primary station and the user observation value corrected by step 5 or 6, and constructs a second Kalman filter including the user position parameter and the ambiguity parameter to perform baseline solution.
  • the step 1 includes the following steps:
  • Step 11 generating a triangulation network by using a Delaunay triangulation algorithm
  • Step 12 extracting a triangulation network boundary reference station and a non-boundary reference station, wherein if the sum of the angles of the inner angles of the vertices of the base station in all the triangles connected to a reference station is greater than a set threshold, the base station a non-boundary base station, otherwise a boundary reference station;
  • Step 13 initializing the star network element set: all the non-boundary base stations are used as the primary station, and all the base stations connected thereto are used as the corresponding auxiliary stations;
  • Step 14 Find a set of base stations that do not participate in the star network networking in the boundary reference station;
  • Step 15 selecting the base station with the largest number of base stations connected to it as the new primary station in the base station set not participating in the networking, and the base station connected thereto as the corresponding secondary station, updating the star network set and not a set of base stations participating in a star network network;
  • Step 16 Repeat step 15 until the number of base stations that are not participating in the star network networking is zero.
  • the step 2 includes the following steps:
  • Step 21 The base station k receives the pseudorange and carrier observation signals of the satellite s, and the carrier and pseudorange observation equations are expressed as:
  • Step 22 according to the carrier observation value obtained by step 21 and the pseudorange observation value constitute a double difference observation value, the double difference carrier observation equation and the double difference pseudo range observation equation of the reference station k and the reference station y are respectively:
  • a double-difference carrier observation indicating the j-th frequency point Indicates the star distance of the double-difference station, Indicates the double-difference full-circumference ambiguity at the jth frequency point, Indicates double-difference tropospheric delay, Indicates the doubled ionospheric delay at the first frequency point, Means the double-difference carrier multipath effect in meters on the jth frequency point, Indicates the double-difference carrier observation noise in meters on the jth frequency point, Indicates the double-difference pseudorange observation at the jth frequency point, Representing the double-difference pseudorange multipath effect at the jth frequency point, Representing the double difference pseudorange observation noise at the jth frequency point;
  • Step 23 Combine the double-difference observations formed in step 22 into a double-difference wide lane combined observation value, and calculate the wide lane combined ambiguity:
  • Step 24 using a combination of no ionosphere, constructing a narrow lane filter, and separating the base ambiguity by using a combination of no ionosphere and wide lane versus Includes the following steps:
  • step 241 a combined observation of the double-difference ionosphere is formed:
  • Step 242 constructing a first Kalman filter:
  • E(g) represents the mathematical expectation and Cov(g) represents the covariance.
  • the base station k and the reference station y have n GPS satellites and g BDS satellites, wherein the nth GPS satellite and the g-th BDS satellite are reference stars of each system.
  • the estimation parameters include double-difference ambiguity and baseline relative zenith tropospheric wet delay.
  • the filter model to be estimated parameter vector, observation vector and design matrix are expressed as:
  • Step 243 Perform filtering according to the first Kalman filter established in step 242, and solve a basic carrier ambiguity vector parameter.
  • Step 25 according to the result of step 24, generating a baseline double difference atmospheric error:
  • the step 3 includes the following steps:
  • Step 31 The primary station and the secondary station of the star network element form a baseline, and the baseline consisting of the same base station is searched in the baseline list of the entire network of the triangular network. If the two baseline directions are opposite, the baseline commutation is required, thereby obtaining Baseline double-difference atmospheric error for each type of star network element;
  • Step 32 Check whether the baseline reference stars of the star network elements are consistent. If they are inconsistent, the reference star is unified and the reference star transformation is performed on the atmospheric error;
  • Step 33 Generate a single-difference atmospheric error between the star-type network elements: the single-difference atmospheric error between the reference satellite stations is 0, and the single-difference atmospheric error value of the other satellites is equal to the double-difference atmospheric error;
  • Step 34 Extract satellites of each star-shaped network element that can be shared by the baseline, and generate a single-difference atmospheric error between the satellite stations of the star-shaped network element.
  • the method for determining the network element where the step 5 is located is: the user decodes the received data and calculates the linear distance of the user to each primary station, represented by the primary station with the smallest distance.
  • the NE is the NE of the user.
  • the step 5 or 6 interpolates the difference between the station and the primary station according to the differential data of the network element in which the network element is located, and corrects the observation to the primary station observation value, including the following steps:
  • Step 1 Using the linear interpolation algorithm to interpolate the user's atmospheric error, the single-difference atmospheric error between the user and the primary station is calculated as follows:
  • m denotes the primary station
  • u denotes the user
  • ⁇ 1 , ⁇ 2 , ⁇ 3 denote the tropospheric interpolation coefficient
  • ⁇ 1 , ⁇ 2 denote the ionospheric interpolation coefficient
  • w denote the number of secondary stations
  • tropospheric interpolation coefficients The tropospheric interpolation coefficients and ionospheric interpolation coefficients are calculated as follows:
  • step 2 the atmospheric error value obtained by the interpolation is corrected to the observation value of the primary station, and the pseudorange and the carrier observation value are respectively as follows:
  • the double difference observation in the step 7 is expressed as:
  • Representing GPS satellite double-difference pseudorange observations Indicates the satellite distance of the GPS satellite double-difference station, Representing GPS satellite double-difference pseudorange multipath effect, Representing GPS satellite double-difference pseudorange observation noise, Indicates the GPS satellite double-difference carrier observation, and ⁇ G represents the wavelength of the GPS observation corresponding to the frequency point.
  • Represents the GPS satellite double-difference ambiguity Represents the GPS satellite double-difference carrier multipath effect in weeks, Representing GPS satellite double-difference carrier observation noise in weeks, Indicates the double-difference pseudorange observation of the BDS satellite, Indicates the star distance of the BDS satellite double-difference station, Representing the double-signal pseudorange multipath effect of BDS satellites, Representing BDS satellite double-difference pseudorange observation noise, Indicates the BDS satellite double-difference carrier observation, and ⁇ C represents the wavelength of the BDS observation corresponding to the frequency point. Indicates the double-difference ambiguity of the BDS satellite, Representing the double-signal carrier multipath effect of the BDS satellite in weeks, Indicates the BDS satellite double-difference carrier observation noise in weeks.
  • the second Kalman filter in step 7 is constructed as follows:
  • the user and the star network have n GPS satellites and g BDS satellites, of which the nth GPS satellite and the gth BDS satellite are reference stars of each system, and all satellites are combined.
  • L1 carrier and P1 pseudorange observation data, filter model to be estimated parameter matrix Observation matrix Design matrix Expressed as:
  • Three-dimensional coordinate parameter vector and floating-point ambiguity parameter vector The coordinate parameter vector after fixing the ambiguity, To fix the ambiguity parameter vector, Corresponding to each parameter filtering solution covariance matrix.
  • the present invention has the following technical effects:
  • the present invention uses a star network for atmospheric interpolation to provide redundant observations
  • the present invention truly realizes the broadcast mode network RTK, and takes into consideration two-way communication, and the user capacity is not limited;
  • the differential data can be broadcasted by ground equipment or by air plane or satellite, and can be used for wide-area differential and satellite-based enhanced data broadcast.
  • FIG. 1 is a flow chart of a BDS/GPS broadcast network RTK algorithm based on a star network according to the present invention.
  • FIG. 2 is a flow chart of a star network element generation algorithm.
  • the invention designs a BDS/GPS broadcast network RTK algorithm based on a star network, as shown in FIG. 1 , comprising the following steps:
  • Step 1 Obtain the base station coordinates from the database, use the Delaunay triangulation algorithm to generate the Delaunay triangulation, and generate a star-shaped network composed of several star network elements and control the whole region on the basis of the triangulation network, wherein each A star network element consists of a primary station and a number of secondary stations corresponding to it.
  • the step 1 includes the following steps:
  • Step 11 using a Delaunay triangulation algorithm to generate a triangulation, wherein the maximum angle threshold of the triangle is 165° (the threshold is set according to personal experience);
  • Step 12 extracting a triangulation network boundary reference station and a non-boundary reference station, wherein if the sum of the angles of the inner angles of the vertices of the base station in all the triangles connected to a reference station is greater than a set threshold of 195°, then The base station is a non-boundary base station, otherwise it is a boundary base station;
  • Step 13 initializing the star network element set: all the non-boundary base stations are used as the primary station, and all the base stations connected thereto are used as the corresponding auxiliary stations;
  • Step 14 Find a set of base stations that are not participating in the star network networking in the border reference station (neither the primary station nor the primary station);
  • Step 15 selecting the base station with the largest number of base stations connected to it as the new primary station in the base station set not participating in the networking, and the base station connected thereto as the corresponding secondary station, updating the star network set and not a set of base stations participating in a star network network;
  • Step 16 Repeat step 15 until the number of base stations that are not participating in the star network networking is zero.
  • Step 2 Obtain the base station data in real time, form a double-difference observation with the baseline in the Delaunay triangulation, construct a first Kalman filter, estimate the baseline double-difference ambiguity and the zenith tropospheric wet delay ZTD in real time, and then generate each Baseline double difference atmospheric error for the baseline.
  • Step 2 includes the following steps:
  • Step 21 The base station k receives the pseudorange and carrier observation signals of the satellite s, and the carrier and pseudorange observation equations are expressed as:
  • Step 22 according to the carrier observation value obtained by step 21 and the pseudorange observation value to form a double difference observation value, the double difference carrier observation equation and the double difference pseudo range observation equation of the reference station k and the reference station y are respectively:
  • a double-difference carrier observation indicating the j-th frequency point Indicates the star distance of the double-difference station, Indicates the double-difference full-circumference ambiguity at the jth frequency point, Indicates double-difference tropospheric delay, Indicates the doubled ionospheric delay at the first frequency point, Means the double-difference carrier multipath effect in meters on the jth frequency point, Indicates the double-difference carrier observation noise in meters on the jth frequency point, Indicates the double-difference pseudorange observation at the jth frequency point, Representing the double-difference pseudorange multipath effect at the jth frequency point, Indicates the double-difference pseudorange observation noise at the jth frequency point.
  • step 23 the double-difference observations formed in step 22 are combined into a double-difference wide lane combined observation value to solve the wide lane combined ambiguity.
  • the MW combination is used to solve the wide lane ambiguity, it is only affected by the carrier and pseudorange observation noise (ignoring multipath), and the observation noise obeys the Gaussian white noise distribution. Therefore, the multi-epoch smoothing rounding can be adopted for the wide lane combination ambiguity.
  • the specific formula is as follows:
  • Step 24 using a combination of no ionosphere, constructing a narrow lane filter, and separating the base ambiguity by using a combination of no ionosphere and wide lane versus Includes the following steps:
  • step 241 a combined observation of the double-difference ionosphere is formed:
  • Step 242 constructing a first Kalman filter:
  • E(g) represents the mathematical expectation and Cov(g) represents the covariance.
  • the base station k and the reference station y have n GPS satellites and g BDS satellites, wherein the nth GPS satellite and the g-th BDS satellite are reference stars of each system.
  • the estimation parameters include double-difference ambiguity and baseline relative zenith tropospheric wet delay.
  • the filter model to be estimated parameter vector, observation vector and design matrix are expressed as:
  • Step 243 Filter according to the first Kalman filter established in step 242, and solve the basic carrier ambiguity vector parameter.
  • the ambiguity obtained here is the full-circumference ambiguity at the first frequency point).
  • satellites with different elevation angles adopt a weighting method based on satellite elevation angle.
  • the zenith tropospheric wet delay adopts random walk.
  • the receiver observation noise obeys Gaussian white noise distribution, and the ambiguity is determined as time-invariant parameter.
  • Step 25 according to the result of step 24, generating a baseline double difference atmospheric error:
  • step 3 the generated baseline atmospheric error is assigned to the corresponding star network baseline, and the baseline direction transformation is performed if necessary.
  • the star-shaped network is used as a unit to unify the reference star and carry out the reference star transformation to generate a single-difference atmospheric error between the star-shaped network elements and the satellite stations.
  • step 3 generating a single-difference atmospheric error between the star network stations includes the following steps:
  • a star network primary station and a secondary station form a baseline, and a baseline consisting of the same base station is searched in the entire network baseline list. If the two baseline directions are opposite, a baseline commutation is required. This results in a baseline atmospheric error for the star network.
  • Step 42 Check whether the baseline reference stars of the star network elements are consistent. If they are inconsistent, select a unified reference star and perform a reference star transformation on the atmospheric error.
  • step 43 a single difference atmospheric error between the star network elements is generated. If the single-difference atmospheric error between the reference satellite stations is 0, the inter-station single-difference atmospheric error value of other satellites is equal to the double-difference atmospheric error.
  • Step 44 Extract all the satellites of the star network element that are common to each baseline, and generate a single-difference atmospheric error between the satellite stations of the star-shaped network element.
  • Step 4 Using the star network element as a unit, encoding the single-difference atmospheric error, all base station coordinates, and the observation value of the primary station for each baseline satellite station of the star network element, and broadcasting by using the UDP protocol (broadcasting
  • the mode can be broadcasted by equipment such as ground equipment, aviation aircraft or satellite) or two-way communication (the user can upload the approximate location information, and only broadcast the data of the user's star network element to the user through Ntrip protocol or TCP/IP according to the user's location) .
  • GNSS Beidou/Global Satellite Navigation System
  • II Beidou/Global Satellite Navigation System
  • BDS MSM4 observations 1050 BDS ionospheric correction value single difference between stations 1051 BDS geometric correction value single difference between stations Tropospheric delay in the present invention 1074 GPS MSM4 observations 1015 GPS ionospheric correction value single difference between stations 1016 GPS geometric correction value single difference between stations Tropospheric delay in the present invention 1006 Master station ECEF coordinate information 1033 Master station receiver and antenna information 1014 Secondary station coordinate information Coordinate difference between the auxiliary station and the main station 1013 Status and frequency information of message broadcast
  • Step 5 The user receives the data in real time and judges the network element where it is located, and interpolates the difference between the station and the primary station according to the difference data of the network element in which the network element is located, and corrects the error to the observation value of the primary station, and proceeds to step 7. Wherein, when the user decodes the received data and calculates the linear distance of the rover to each primary station, the network element represented by the primary station with the smallest distance is used as the network element where the mobile station is located.
  • step 6 the user receives the data in real time, and interpolates the difference between the station and the main station according to the differential data of the network element in which the network element is located, and corrects the error to the observation value of the primary station, and proceeds to step 7.
  • step 5 or 6 according to the differential data of the network element in which the network element is interpolated, the single-difference atmospheric error between the station and the primary station is obtained, and corrected to the observation value of the primary station, including the following steps:
  • Step 1 Using the linear interpolation algorithm to interpolate the user's atmospheric error, the single-difference atmospheric error between the user and the primary station is calculated as follows:
  • m denotes the primary station
  • u denotes the user
  • ⁇ 1 , ⁇ 2 , ⁇ 3 denote the tropospheric interpolation coefficient
  • ⁇ 1 , ⁇ 2 denote the ionospheric interpolation coefficient
  • w denote the number of secondary stations
  • tropospheric interpolation coefficients The tropospheric interpolation coefficients and ionospheric interpolation coefficients are calculated as follows:
  • step 2 the atmospheric error value obtained by the interpolation is corrected to the observation value of the primary station, and the pseudorange and the carrier observation value are respectively as follows:
  • step 7 the user constructs a double-cavity observation using the observation of the primary station corrected by step 5 and the user observation (received by the user receiver itself) to construct a second Kalman filter including the user position parameter and the ambiguity parameter. , perform a baseline solution.
  • the baseline solution in step 7 includes the following steps:
  • step 71 a double difference observation equation is formed, and the pseudorange and carrier double difference observation equation can be expressed as:
  • Representing GPS satellite double-difference pseudorange observations Indicates the satellite distance of the GPS satellite double-difference station, Representing GPS satellite double-difference pseudorange multipath effect, Representing GPS satellite double-difference pseudorange observation noise, Indicates the GPS satellite double-difference carrier observation, and ⁇ G represents the wavelength of the GPS observation corresponding to the frequency point.
  • Represents the GPS satellite double-difference ambiguity Represents the GPS satellite double-difference carrier multipath effect in weeks, Representing GPS satellite double-difference carrier observation noise in weeks, Indicates the double-difference pseudorange observation of the BDS satellite, Indicates the star distance of the BDS satellite double-difference station, Representing the double-signal pseudorange multipath effect of BDS satellites, Representing BDS satellite double-difference pseudorange observation noise, Indicates the BDS satellite double-difference carrier observation, and ⁇ C represents the wavelength of the BDS observation corresponding to the frequency point.
  • Step 72 constructing a Kalman filter, comprising the following steps:
  • the user and the star network have n GPS satellites and g BDS satellites, of which the nth GPS satellite and the gth BDS satellite are reference stars of each system, and all satellites are combined.
  • L1 carrier and P1 pseudorange observation data, filter model to be estimated parameter matrix Observation matrix Design matrix Expressed as:
  • the satellites with different elevation angles adopt the weighting method based on the satellite elevation angle.
  • the three-dimensional coordinate parameters adopt random walk.
  • the receiver observation noise obeys the Gaussian white noise distribution, and the ambiguity is determined as the time-invariant parameter.
  • Step 72 after fixing the ambiguity, using the following formula to solve the fixed solution of the user's three-dimensional coordinates:
  • the filtering solution of the medium-long baseline can be realized by designing the wide lane and narrow lane filtering model, taking into account the tropospheric delay and the ionosphere.
  • Network RTK technology is one of the most widely used technologies in the field of high precision real-time positioning. Based on the star network, the method uses the double difference mode for network solution, and the model is simple and the parameters are few.
  • the broadcast mode RTK differential correction number is used in the broadcast mode, and the two-way communication is taken into consideration, and the network RTK of the broadcast mode is truly realized.
  • the role of the rover is fully utilized, so that the rover can use a more optimized algorithm.
  • baseline solution taking into account the network RTK and the traditional RTK mode, it can ensure that the central solution solver does not complete the network initialization, the rover uses the original observation of the primary station for single baseline solution.
  • the invention satisfies both the ordinary network RTK users and the special domain users, such as the military field in which the user coordinates cannot be exposed, and the user capacity is not limited.
  • broadcast mode differential data can be broadcast either by ground equipment or by air or satellite. There are no shortcomings of VRS technology, FKP technology, and MAC technology.

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
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Abstract

L'invention concerne un algorithme RTK de réseau de diffusion BDS/GPS fondé sur un réseau en étoile. Toutes les stations de base forment un réseau de triangulation à l'aide d'un algorithme de triangulation de Delaunay ; un réseau en étoile à zone pleine commandable formé par une pluralité d'éléments de réseau en étoile est généré en fonction du réseau de triangulation. Des données des stations de base sont obtenues en temps réel et un calcul d'élément de réseau est effectué afin de générer une erreur atmosphérique de ligne de base. De plus, en fonction d'un protocole UDP, un serveur diffuse une valeur d'observation de station maîtresse, des coordonnées de station de base et l'erreur atmosphérique de ligne de base pour un utilisateur à l'aide du réseau en étoile en tant qu'unité ; l'utilisateur sélectionne, en fonction d'un emplacement approximatif de ce dernier et de l'emplacement de la station maîtresse, un élément de réseau dans lequel se trouve l'utilisateur ; une interpolation est effectuée sur une erreur atmosphérique à différence unique entre stations de la ligne de base formée par l'utilisateur et la station maîtresse, et l'erreur atmosphérique obtenue est corrigée vers la valeur d'observation de la station maîtresse afin d'effectuer un calcul de ligne de base. L'utilisateur peut également téléverser des coordonnées approximatives au moyen d'une communication bidirectionnelle, le serveur diffusant des données différentielles de l'élément de réseau dans lequel se trouve l'utilisateur. De plus, une diffusion de données différentielles par un équipement tel qu'un équipement au sol, un avion ou un satellite est également prise en charge.
PCT/CN2018/078492 2017-09-26 2018-03-09 Algorithme rtk de réseau de diffusion bds/gps fondé sur un réseau en étoile WO2019062030A1 (fr)

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