CN109932735A - The localization method of the short baseline single-frequency simple epoch solution of Beidou - Google Patents
The localization method of the short baseline single-frequency simple epoch solution of Beidou Download PDFInfo
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Abstract
The invention discloses a kind of localization methods of the short baseline single-frequency simple epoch solution of Beidou, comprising: (1) Beidou single-frequency pseudorange One-Point Location；(2) data prediction；(3) building Kalman filtering precision n is influenced on observation and ties up dynamic model based on priori historical data and consideration signal-to-noise ratio, elevation of satellite；(4) model initialization；(5) estimation of Beidou single-frequency obscure portions degree is carried out, the fixed value of each Hierarchical Fuzzy degree is obtained；(6) it combines single-frequency fuzziness fixed value and updated value to carry out cycle slip to detect by epoch；(7) basic lineal vector calculating and coordinate covariance matrix update are carried out based on fixed fuzziness, after fuzziness is fixed, back substitution carrier phase observational equation obtains basic lineal vector.This method can eliminate the influence of tropospheric error and Multipath Errors in real time automatically, realize the accurate fixation of Beidou single-frequency fuzziness, while guaranteeing monitoring real-time and precision, greatly reduce monitoring cost, be conducive to the popularization and application of Beidou location technology.
Description
Technical field
The present invention relates to the Beidou deformation monitoring field of engineering survey, in particular to a kind of short baseline single-frequency single epoch of Beidou
The localization method of resolving.
Background technique
Beidou satellite navigation system (BeiDou Navigation Satellite System, abbreviation Beidou) at present, can
Navigation, time service and short message information service are provided for the whole world, application of the Beidou location technology in deformation monitoring field has become
One of research hotspot.Cost is the critical issue that Beidou deformation monitoring needs to solve, and it is more that Beidou deformation monitoring generallys use multifrequency
Mould geodetic type receiver, cost is excessively high seriously to limit Beidou in the popularization and application in deformation monitoring field, for job facilities office
The baseline length of portion's deformation monitoring, base station and monitoring station is generally shorter (≤10km).Therefore, one kind is studied to connect based on low cost
The short baseline single-frequency single epoch high-precision relative localization algorithm of the Beidou of receipts machine has important practical significance.
Lot of domestic and international scholar has conducted extensive research Beidou location algorithm.Odolinski R, Teunissen P J
G and Odijk D (First combined COMPASS/BeiDou-2and GPS positioning results in
Australia.Part I:single-receiver and relative code-only positioning[J]
.Journal of Spatial Science, 2014,59 (1): 3-24.) for the first time Australian area inquired into Beidou and
As a result GPS integrated positioning performance shows further integrated positioning ambiguity resolution success rate and reliability of positioning much higher than single
Satellite system (Beidou or GPS)；Hu Zhigang (Beidou satellite navigation system Performance Evaluation theory and the Wuhan verification experimental verification [D]: military
Chinese university, 2013) by analyzing dipper system measured data, Beidou and the short baseline of GPS are relatively fixed under the identical environment of comparative analysis
As a result, the discovery short baseline positioning precision of Beidou can reach mm grades, accurate one-point PPP can reach cm for position and Static Precise Point Positioning
Class precision, positioning performance are substantially close with GPS；Gao Xingwei etc. (merges positioning [J] based on the unified Beidou of space-time system with GPS
Journal is surveyed and drawn, 2012,41 (5): 743-748,755) Beidou and time system/coordinate system unification of GPS are had studied, it realizes
The data fusion and high-precision alignment by union of Beidou and GPS carrier phase, prove finally by measured data and processing result
Beidou tentatively has a three-dimensional high-precision stationkeeping ability；(the Instantaneous BeiDou+GPS such as Teunissen P J G
RTK positioning with high cut-off elevation angles[J].Journal of Geodesy,
2014,88 (4): 335-350) propose single epoch GPS/ Beidou single-frequency and double frequencies RTK (Real Time Kinematic) combination
Location model, in the case of demonstrating 40 ° of satellite altitude angle of cut-off, GPS/ Beidou double frequency built-up pattern is still able to achieve high-precision fixed
Position.
However the studies above is both for the integrated positioning of the Beidou of multi-frequency multi-mode receiver, Beidou and GPS, and for north
The high accuracy positioning research of bucket single frequency receiving, especially the static immobilization technique study of the short baseline single-frequency simple epoch solution of Beidou
It is less.
Summary of the invention
The purpose of the present invention is overcoming the above-mentioned prior art, Beidou single-frequency precision function and random weighting mould are constructed
Type, provides a kind of localization method that Beidou single-frequency single epoch Kalman filtering resolves, and this method can be realized Beidou single-frequency list and go through
First static high-precision resolves, improves the fixed success rate of Beidou single-frequency fuzziness, positioning accuracy and reliability, accurate detection displacement hair
The raw period, suitable for the engineering structural system with creeping characteristic.
For this purpose, technical scheme is as follows:
A kind of localization method of the short baseline single-frequency simple epoch solution of Beidou, comprising the following steps:
(1) Beidou single-frequency pseudorange One-Point Location:
According to pseudorange double-difference equation, calculate movement station initial coordinate using Bancroft algorithm, using Klobuchar and
Saastamoinen model correction ionosphere and tropospheric error carry out least square then using the result as approximate coordinate
Linearization calculation monitoring station, benchmark station coordinates and elevation of satellite:
P (t)=ρ (t)+I (t)+T (t)+v (t)
Wherein, P is the Pseudo-range Observations of epoch t moment, and ρ is that big-dipper satellite arrives the distance between survey station, and I is ionosphere mistake
Difference, T are tropospheric error, and v is observation noise, and the unit of each parameter is m,
(2) data prediction:
It is influenced based on Beidou single frequency receiving observation vulnerable to cycle slip, multipath equal error, satellite altitude angle of cut-off is set,
The elevation angle of satellite is calculated according to step (1), rejects the satellite for being lower than the angle, is observed using fitting of a polynomial carrier phase
Cycle slip is repaired in value, preliminary detection.In one embodiment of the invention, setting satellite altitude angle of cut-off is 20 °.
(3) building Kalman filtering is influenced to observation based on priori historical data and consideration signal-to-noise ratio, elevation of satellite
Accurate n ties up dynamic model, comprising the following steps:
1) priori data is combined to construct system dynamic model:
Using the vector sum float-solution of priori historical data estimation current basic line, basic lineal vector, velocity vector and double are chosen
Fuzziness ties up dynamic model expression as system mode vector, n are as follows:
Wherein,For monitor station coordinates, unit m,For dynamic model noise error, unit m,For double difference
Fuzziness, unit cycles, n are different types of motion model, and n=0 indicates that monitoring station is in static or creep state, n
=1 is in uniform speed motion state, and n=2 indicates to be in accelerated motion state, and r indicates that base station, rm indicate monitoring station, and P indicates ginseng
Examine satellite, s indicate it is observed that the big-dipper satellite in addition to reference satellite；
When monitoring station is in creep state, n value is zero, and the system mode transposition model definition of system dynamic model is such as
Under:
2) the influence building based on Beidou single-frequency carrier phase observation and consideration signal-to-noise ratio, elevation of satellite to observation
Beidou single-frequency precision function model and accurate random weighting model, the method for constructing accurate random weighting model are as follows:
It is every suitable power ratio of satellite observation distribution based on big-dipper satellite observation correlation and error statistics characteristic,
Consider big-dipper satellite elevation angle and observation SNR influence, construct Beidou precision random weighting model, obtain optimum linearity without
Estimation partially:
Wherein, CN0 is Big Dipper satellite signal signal-to-noise ratio, and e is elevation of satellite, and unit is ° that s1 is snr threshold, single
Position is db, is generally set to 50db, as CN0 >=s1, observation weight DCN0,eIt is set as 1, it is believed that observation quality is preferable at this time, letter
The ratio s0 that makes an uproar is determined that empirical parameter A, it is 30,20,10 that a, s0, which distinguish value, by empirical parameter A；
(4) model initialization:
Using the result of step (1) as the original state variable [X of Kalman modelr(0)Yr(0)Zr(0)], velocity vectorIt is initialized as zero, it is assumed that monitoring station is moved since stationary state or speed is one constant
Value, the fuzziness of initial epoch is with the rounding of least-squares calculation float-solution, or combines the approximate distance and carrier wave phase between star station
Position double difference observation is calculated；Estimated to update state vector, double difference observation and float ambiguities association side according to double-difference equation
Poor matrix calculates fuzziness and corresponding variance in conjunction with double star station spacing and carrier phase if equation observed quantity is insufficient:
In formula, For the double difference star station spacing obtained by approximate coordinate computation, unit m,For
The evaluated error unit of approximate coordinate error propagation is m,
Ambiguous estimation degreeAre as follows:
Initial state vector covariance matrixIs defined as:
In formula,For initial coordinate estimate variance, unit m2, provided by coordinate covariance matrix； For velocity variance, unit m2/s2；For initial fuzziness estimate variance, unit cycles2；
(5) Beidou single-frequency obscure portions degree is carried out by additional structure deformation behaviour and the constraint of maximum displacement variable quantity to estimate
Meter, obtains the fixed value of each Hierarchical Fuzzy degree；
(6) it combines single-frequency fuzziness fixed value and updated value to carry out cycle slip to detect by epoch, it is right that generation cycle slip needs again
Cycle slip satellite occurs to be initialized:
For low-cost receiver, carrier phase observation data causes vulnerable to multipath, antenna phase deviation, electronic component
Noise equal error influence, while with cycle slip generation, seriously affect the positioning accuracy of low-cost receiver.Kalman filter
Wave post-processing can eliminate influence of the multipath equal error to positioning accuracy, can and realize the key of high accuracy positioning and be cycle slip
Whether can accurately detect.
Based on Kalman dynamic model, fuzziness is not varied over, therefore, the fuzziness that epoch t updates are as follows:
According to covariance matrixThe corresponding fuzziness variance of Satellite s, which obtains, updates fuzziness variance
The fuzziness of epoch t moment more new estimation is calculated by following formula:
The corresponding variance of the fuzziness is
In order to detect cycle slip, tested using following formula:
In formula,It can rule of thumb choose,
If detecting cycle slip, respective satellite can be handled as the epoch newly added satellite, the mould of this satellite
Paste degree retightens；If cycle slip occurs for reference satellite, the epoch all double difference observations and fuzziness should be reinitialized back
To step (1), the calculating of Kalman filtering single epoch basic lineal vector is re-started；
(7) basic lineal vector calculating and coordinate covariance matrix update are carried out based on step (5) fixed fuzziness, wherein sitting
Mark covariance matrix is expressed asAfter fuzziness is fixed, back substitution carrier phase observational equation obtains basic lineal vector:
Wherein, HΦFor the coefficient matrix of basic lineal vector；RΦFor the covariance matrix of double difference carrier phase observation data, according to step
Suddenly (3) accurate stochastic model determines；For double difference carrier phase estimated value, unit m,For double difference fuzziness fixed value,
Unit is cycles.
Above-mentioned steps 2) in, the method for constructing Beidou single-frequency precision function model is as follows:
Epoch t, base station m and monitoring station r can observe k big-dipper satellite, using satellite p as reference satellite, then every satellite
S is expressed as relative to the Beidou single-frequency carrier phase function double difference model of reference satellite:
Wherein, s=1 ... k, s ≠ p, λ be Beidou single-frequency carrier phase wavelength, unit m,It is single for double difference fuzziness
Position is cycles,For double difference carrier phase observation noise, unit m,Respectively double difference survey station satellite
Between geometric distance, double difference ionosphere, tropospheric error, unit m.
For baseline length < 10km, ionosphere and troposphere can be completely eliminated by double difference, and function model simplifies are as follows:
Function model linearisation building Beidou single-frequency Kalman's observation model, observation vector indicate are as follows:
Wherein,The respectively linearisation coefficient of double difference carrier phase geometric distance, [X'r Y'r
Z'r] it is respectively the corresponding velocity vector of monitoring station coordinate, unit m/s.
In above-mentioned step (5), the method for constructing Beidou single-frequency precision function model is as follows:
1. fuzziness float-solution variance-covariance matrix is sought according to least square variance estimation criterion, according to fuzziness
Precision utilizes (2,1 ..., 1)k-1Hierarchical pattern carries out dimensionality reduction step by step and resolves；
2. joint pseudorange, carrier phase observation vector construct the corresponding error observational equation of every level-one fuzziness group, utilize
LAMBDA searches for 50 groups of successively optimal fuzzinesses, is rejected based on the quasi-definite structural body maximum displacement of Numerical-Mode and deformation behaviour
Fuzziness group outside priori basic lineal vector and calculating vector value range；
3. searching for the corresponding integer ambiguity vector of least residual quadratic sum in remaining fuzziness chosen candidate value, selection passes through
Fixed value of the fuzziness candidate value that Ratio is examined as each Hierarchical Fuzzy degree.
In above-mentioned step 1), it is assumed that monitoring station is in creep state, and n value is zero, while the model is also applied for supervising
The system mode transposition model of other motion states of survey station, system dynamic model is defined as follows:
In above-mentioned step 2), snr threshold s1 is 50db, as CN0 >=s1, observation weight DCN0,eIt is set as 1, is recognized
Preferable for observation quality at this time, empirical parameter A, it is 30,20,10 that a, s0, which distinguish value,.
The invention has the following advantages:
1, location algorithm of the invention significantly reduces monitoring cost while guaranteeing monitoring real-time and precision,
Beidou location technology be may advantageously facilitate in the popularization and application in deformation monitoring field.
2, the algorithm efficiently solve big-dipper satellite configuration it is poor when, Beidou single-frequency least squares approximation results deviation is big or cannot
The problem of calculating, can eliminate the influence of tropospheric error and Multipath Errors in real time automatically, realize Beidou B1 frequency single-frequency mould
The accurate fixation of paste degree, can effectively detecting structure body change in displacement and generation period, be China's infrastructure safe operation
Valid data and technical support are provided.
Detailed description of the invention
Fig. 1 is the flow chart of localization method of the present invention；
The point arrangement schematic diagram stood on the basis of Fig. 2 with monitoring station；
Fig. 3 is that Beidou single-frequency single epoch static state resolves result curve figure；
Fig. 4 is different resolving period Beidou single-frequency positioning precision analysis figures；
Fig. 5 is that elevation direction is displaced detection accuracy analysis chart.
Specific embodiment
Technical solution for a better understanding of the present invention, below in conjunction with the drawings and specific embodiments to positioning of the invention
Algorithm is described in detail.
Embodiment one
As shown in Fig. 2, base station and monitoring station are laid in Chinese Railway design Group Co., Ltd's Surveying And Mapping Institute CORS respectively
Station top and Chinese Railway design total No. 3 roofs of institute of Group Co., Ltd, and UM440 Beidou single-frequency board built in receiver can
Receive B1 frequency satellites signal, sample frequency 1HZ.
Referring to Fig. 1 and Fig. 2, in the present embodiment, the localization method of the short baseline single-frequency simple epoch solution of Beidou includes following step
Suddenly
(1) Beidou single-frequency satellite ephemeris and observation file are read, Beidou single-frequency pseudorange equation calculation elevation of satellite is based on；
(2) it is arranged 20 ° of satellite altitude angle of cut-off, rejects the satellite for being lower than the elevation angle, is based on Beidou single-frequency pseudorange single-point
Positioning result and Beidou carrier phase observational equation are observed value Detection of Cycle-slip using polynomial fitting method and repair pre- place
Reason, threshold value are set as 1cycles.
(3) priori epoch data are combined to construct system dynamic model, n value is taken as 0, indicates that monitoring station is in static or creep
State.
Based on Beidou single-frequency carrier phase observation and consider that the influence of signal-to-noise ratio, elevation of satellite to observation constructs north
Struggle against single-frequency function and accurate stochastic model, and snr threshold s1 is set as 50db, and empirical parameter A, it is 30,20 that a, s0, which distinguish value,
10.The accurate stochastic model finally calculated are as follows:
(4) initial epoch needs to carry out model initialization, carries out coordinate and mould in conjunction with pseudorange and carrier phase observational equation
Paste degree resolves, and obtains initial epoch coordinate and fuzziness covariance matrix, if equation redundancy is insufficient, in conjunction with double star station spacing and
Carrier phase calculates fuzziness and corresponding variance.
Fuzziness primary standard difference is set as 1000, and unit cycles, B1 frequency pseudorange primary standard difference is set as 0.3m, carries
Wave phase primary standard difference is set as 0.003m, constructs the state transposed matrix of basic lineal vector coordinate and fuzziness, T=I38×38, most
Small observation satellite number is set as 4, and it is 8 that this, which tests initial epoch year Beidou satellites in view number, according to pseudorange and carrier phase side
It is respectively (- 2263559.6610,4404125.3541,4006546.6488), unit that journey, which calculates the initial epoch coordinate in monitoring station,
For m, monitoring station coordinates covariance matrix isDan Wei ㎡, double difference fuzziness variance-covariance
Matrix isUnit is
cycle2, initial state vector coordinate and float ambiguities are [- 2263559.6610 4404125.3541 4006546.6488
-20.6211 -2.3515 -16.1747 0.1479 4.8535 -7.5936 4.2049 0 0 0 -0.3629 0 0 0]T,
State transition matrix is 17 × 17 unit matrixs；
(5) float ambiguities progress obscure portions degree is searched for step by step, while additional structure deformation behaviour and dominant bit
Moving variable quantity and constraining fixed Beidou single-frequency double difference fuzziness is [- 20-3-16 067400 0-1 00 0]；
(6) double difference pseudorange and carrier phase observation data ambiguous estimation degree approximation are based on are as follows:
[-20.5622 -2.3397 -16.1558 0.2501 4.9794 -7.5802 4.2114 0 0 0 -0.3592
00 0], compare and find with step (5) fixed value, cycle slip occurs for big-dipper satellite PRN5, this satellite double difference fuzziness needs weight
Newly initialized；(7) it is based on the Beidou single-frequency fuzziness fixed value of step (6), calculates initial epoch basic lineal vector fixed solution
For [- 2263559.5267 4404125.1375 4006546.6214], in conjunction with step (2) and (4) observation and state vector
Variance matrix carries out Kalman filtering and seeks next epoch filtering updated value and state vector covariance, by epoch carry out baseline to
Amount calculates, and after result is as shown in figure 3, fuzziness is fixed, positioning accuracy can achieve 5mm, much higher than traditional RTK positioning accurate
Degree.
Data verification:
Field is monitored further to prove that this method can be applied to high-precision deformation, as shown in figure 4, choosing different resolvings
Period finally fixes epoch coordinate and carries out precision analysis, increase of the discovery with convergence time, the corresponding software of this method
20 hours Beidou single-frequency calculation accuracies of GNSSMonitor v1.0 reach 1.4mm, much higher than the essence of Bernese v5.2 software
Degree；Relative to plane, elevation direction is influenced vulnerable to multipath, observation noise equal error, as shown in figure 5, resolving the period is 12 small
When, in the 7th period load deflection 6mm, 4 period elevation coordinate average values are initially sat as displacement load before choosing load deflection
Mark, rear 4 periods coordinate average value are analyzed, discovery as final coordinate after displacement load with actual loaded displacement comparison
It is 1mm that GNSSMonitor v1.0 software, which is displaced detection accuracy, and Bernese v5.2 software precision is 3mm, while can be accurate
The specific period that displacement occurs is detected, the superiority of this algorithm is further illustrated.
Claims (6)
1. a kind of localization method of the short baseline single-frequency simple epoch solution of Beidou, comprising the following steps:
(1) Beidou single-frequency pseudorange One-Point Location:
According to pseudorange double-difference equation, calculate movement station initial coordinate using Bancroft algorithm, using Klobuchar and
Saastamoinen model correction ionosphere and tropospheric error carry out least square then using the result as approximate coordinate
Linearization calculation monitoring station, benchmark station coordinates and elevation of satellite:
P (t)=ρ (t)+I (t)+T (t)+v (t)
Wherein, P is the Pseudo-range Observations of epoch t moment, and ρ is that big-dipper satellite arrives the distance between survey station, and I is ionospheric error, T
For tropospheric error, v is observation noise, and the unit of each parameter is m,
(2) data prediction:
Satellite altitude angle of cut-off is set, the elevation angle of satellite is calculated according to step (1), rejects the satellite for being lower than the angle, is used
Cycle slip is repaired in fitting of a polynomial carrier phase observation data, preliminary detection,
(3) building Kalman filtering precision n is influenced to observation based on priori historical data and consideration signal-to-noise ratio, elevation of satellite
Tie up dynamic model, comprising the following steps:
1) priori data is combined to construct system dynamic model:
Using the vector sum float-solution of priori historical data estimation current basic line, basic lineal vector, velocity vector and Double Fuzzy are chosen
Degree is used as system mode vector, and n ties up dynamic model expression are as follows:
Wherein,For monitor station coordinates, unit m,For dynamic model noise error, unit m,It is fuzzy for double difference
Degree, unit cycles, n are different types of motion model, and n=0 indicates that monitoring station is in static or creep state, at n=1
In uniform speed motion state, n=2 expression is in accelerated motion state, and r indicates that base station, m indicate monitoring station, and P indicates that reference is defended
Star, s indicate it is observed that the big-dipper satellite in addition to reference satellite；
2) Beidou is constructed based on Beidou single-frequency carrier phase observation and consideration signal-to-noise ratio, influence of the elevation of satellite to observation
Single-frequency precision function model and accurate random weighting model, the method for constructing accurate random weighting model are as follows:
It is every suitable power ratio of satellite observation distribution, consideration based on big-dipper satellite observation correlation and error statistics characteristic
Big-dipper satellite elevation angle and observation SNR influence construct Beidou precision random weighting model, obtain that optimum linearity is unbiased to be estimated
Meter:
Wherein, CN0 is Big Dipper satellite signal signal-to-noise ratio, and e is elevation of satellite, and unit is ° that s1 is snr threshold, and unit is
Db is generally set to 50db, as CN0 >=s1, observation weight DCN0,eIt is set as 1, it is believed that observation quality is preferable at this time, signal-to-noise ratio
Value s0 is determined that empirical parameter A, it is 30,20,10 that a, s0, which distinguish value, by empirical parameter A；
(4) model initialization:
Using the result of step (1) as the original state variable [X of Kalman modelr(0) Yr(0) Zr(0)], velocity vectorIt is initialized as zero, it is assumed that monitoring station is moved since stationary state or speed is one constant
Value, the fuzziness of initial epoch is with the rounding of least-squares calculation float-solution, or combines the approximate distance and carrier wave phase between star station
Position double difference observation is calculated；Estimated to update state vector, double difference observation and float ambiguities association side according to double-difference equation
Poor matrix calculates fuzziness and corresponding variance in conjunction with double star station spacing and carrier phase if equation observed quantity is insufficient:
In formula, For the double difference star station spacing obtained by approximate coordinate computation, unit m,For approximation
The evaluated error unit that error of coordinate is propagated is m,
Ambiguous estimation degreeAre as follows:
Initial state vector covariance matrixIs defined as:
In formula,For initial coordinate estimate variance, unit m2, provided by coordinate covariance matrix； For velocity variance, unit m2/s2；For initial fuzziness estimate variance, unit cycles2；
(5) estimation of Beidou single-frequency obscure portions degree is carried out by additional structure deformation behaviour and the constraint of maximum displacement variable quantity,
Obtain the fixed value of each Hierarchical Fuzzy degree；
(6) it combines single-frequency fuzziness fixed value and updated value to carry out cycle slip to detect by epoch, cycle slip occurs and needs again to generation
Cycle slip satellite is initialized:
Based on Kalman dynamic model, fuzziness is not varied over, therefore, the fuzziness that epoch t updates are as follows:
According to covariance matrixThe corresponding fuzziness variance of Satellite s, which obtains, updates fuzziness variance
The fuzziness of epoch t moment more new estimation is calculated by following formula:
The corresponding variance of the fuzziness is
In order to detect cycle slip, tested using following formula:
In formula,It can rule of thumb choose,
If detecting cycle slip, respective satellite can be handled as the epoch newly added satellite, the fuzziness of this satellite
It retightens；If cycle slip occurs for reference satellite, the epoch all double difference observations and fuzziness should reinitialize and return to step
Suddenly (1) re-starts the calculating of Kalman filtering single epoch basic lineal vector；
(7) basic lineal vector calculating and coordinate covariance matrix update are carried out based on step (5) fixed fuzziness, wherein coordinate is assisted
Variance matrix is expressed asAfter fuzziness is fixed, back substitution carrier phase observational equation obtains basic lineal vector::
Wherein, HΦFor the coefficient matrix of basic lineal vector；RΦFor the covariance matrix of double difference carrier phase observation data, according to step
(3) accurate stochastic model determines；For double difference carrier phase estimated value, unit m,It is single for double difference fuzziness fixed value
Position is cycles.
2. localization method according to claim 1, which is characterized in that in step 2), construct Beidou single-frequency precision Function Modules
The method of type is as follows:
Epoch t, base station m and monitoring station r can observe k big-dipper satellite, using satellite p as reference satellite, then every satellite s phase
The Beidou single-frequency carrier phase function double difference model of reference satellite is expressed as:
Wherein, s=1 ... k, s ≠ p, λ be Beidou single-frequency carrier phase wavelength, unit m,For double difference fuzziness, unit is
Cycles,For double difference carrier phase observation noise, unit m,Respectively between double difference survey station satellite
Geometric distance, double difference ionosphere, tropospheric error, unit m.
For baseline length < 10km, ionosphere and troposphere can be completely eliminated by double difference, and function model simplifies are as follows:
Function model linearisation building Beidou single-frequency Kalman's observation model, observation vector indicate are as follows:
Wherein,The respectively linearisation coefficient of double difference carrier phase geometric distance, [X 'r Y′r Z′r] point
Not Wei the corresponding velocity vector of monitoring station coordinate, unit m/s.
3. localization method according to claim 1, which is characterized in that in step (5), carry out Beidou single-frequency obscure portions degree
The step of estimation, is as follows:
1. fuzziness float-solution variance-covariance matrix is sought according to least square variance estimation criterion, according to fuzziness precision,
Utilize (2,1 ..., 1)k-1Hierarchical pattern carries out dimensionality reduction step by step and resolves；
2. joint pseudorange, carrier phase observation vector construct the corresponding error observational equation of every level-one fuzziness group, utilize
LAMBDA searches for 50 groups of successively optimal fuzzinesses, is rejected based on the quasi-definite structural body maximum displacement of Numerical-Mode and deformation behaviour
Fuzziness group outside priori basic lineal vector and calculating vector value range；
3. searching for the corresponding integer ambiguity vector of least residual quadratic sum in remaining fuzziness chosen candidate value, selection passes through
Fixed value of the fuzziness candidate value that Ratio is examined as each Hierarchical Fuzzy degree.
4. localization method according to claim 1, which is characterized in that assume that monitoring station is in creep state, n in step 1)
Value is zero, while the model is also applied for other motion states of monitoring station, the system mode transposition model of system dynamic model
It is defined as follows:
。
5. localization method described in any one of -4 according to claim 1, which is characterized in that in step (2), satellite altitude is arranged
Angle of cut-off is 20 °.
6. localization method according to claim 5, which is characterized in that in step 2), snr threshold s1 is 50db, when
When CN0 >=s1, observation weight DCN0,eIt is set as 1, it is believed that observation quality is preferable at this time, and empirical parameter A, a, s0 distinguish value and be
30,20,10.
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