CN108121001A - The quasi-static scene positioning accuracy optimization method differentiated based on fixed solution continuity - Google Patents
The quasi-static scene positioning accuracy optimization method differentiated based on fixed solution continuity Download PDFInfo
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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
- G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
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Abstract
The present invention relates to a kind of quasi-static scene positioning accuracy optimization methods differentiated based on fixed solution continuity, belong to Big Dipper high accuracy positioning correlative technology field, the more particularly to optimization of Big Dipper high accuracy positioning fixed solution and positioning accuracy.Including based on Big Dipper high-precision relative localization algorithm, it is identified by simple convolution algorithm and shows stationary state and the larger calculation result of actual error in position fixing process, convolution algorithm acts only on the fixed solution result obtained using mature technology, it is not related to Baselines algorithm, for the practical operation algorithm that continuity differentiates, the displacement monitoring precision under the conditions of quasi-static in Big Dipper high precision monitor type receiver is drastically increased.It proposes a kind of relatively simple fixed solution continuity method of discrimination, can recognize that by simple convolution algorithm and rejects isolated pseudo- fixed solution, effectively promote deformation/displacement monitoring precision of the Big Dipper high precision monitor receiver under the conditions of quasi-static；The deployment of its algorithm is simple, is easy to apply, relatively low on the influence of fixed solution availability, possesses preferable promotion prospect.
Description
Technical field
The present invention relates to a kind of quasi-static scene positioning accuracy optimization methods differentiated based on fixed solution continuity, belong to north
The optimization of bucket high accuracy positioning correlative technology field, more particularly to Big Dipper high accuracy positioning fixed solution and positioning accuracy.
Background technology
With the quickening of Beidou satellite navigation system pace of construction, the development of various Beidou receiver has also welcome a height
Tide, the application scenarios of dipper system have obtained greatly expanding, and progressively possess the ability for substituting GPS system comprehensively.The Big Dipper is high-precision
Degree positioning is one of important application scene of dipper system, is widely used in the necks such as intelligent driving, deformation monitoring, geodesic survey
Domain.
Big Dipper high-precision location technique mainly includes the phase that algorithm is fixed based on carrier phase observed quantity and integer ambiguity
To location algorithm and Static Precise Point Positioning algorithm.Both the above algorithm all must can be only achieved its reason in the case where obtaining fixed solution state
By ultimate precision, there are multiple patents that it is discussed in detail at present, as 61081 army of the Chinese People's Liberation Army declares
《Medium-long baselines Ambiguity Solution Methods based on four frequency signal of the Big Dipper》（The patent No.：CN 102650694 B）, Southeast China University Shen
Report《Fuzziness fast resolution algorithm between a kind of network rtk reference stations of Big Dipper compatibility gps/glonass》（The patent No.：CN
105204048 B）Deng.However the obtained fixed solution after Big Dipper ambiguity of carrier phase is fixed is frequent in practical applications
The problem of appearance and larger reference true value deviation, so as to influence the use of data.Currently available technology is based primarily upon solution of fuzzy degree
Mathematical statistics characteristic test to the reliability of float-solution and fixed solution, such as China University Of Petroleum Beijing（East China）It declares《One
Integer ambiguity validity check method in kind satellite navigation system》（The patent No.：CN105301617B）, shortcoming is it
The definite of decision threshold ratio values needs rule of thumb to set, without the universal standard, while its algorithm operation quantity it is larger, it is necessary to
More deep customized development is carried out to its carrier phase relative localization algorithm, generalization degree is relatively low.
The content of the invention
It is an object of the invention to provide a kind of quasi-static scene positioning accuracy optimizations differentiated based on fixed solution continuity
Method, so as to overcome the deficiencies in the prior art.
The invention is realized by the following technical scheme, including based on Big Dipper high-precision relative localization algorithm, passing through letter
Single convolution algorithm identifies and show stationary state in position fixing process and the larger calculation result of actual error, convolution algorithm
The fixed solution obtained using mature technology is acted on as a result, not being related to Baselines algorithm, the practical operation differentiated for continuity
Algorithm drastically increases the displacement monitoring precision under the conditions of quasi-static in Big Dipper high precision monitor type receiver.
The Big Dipper high-precision relative localization algorithm includes three parts：The reception of Big Dipper signal, information processing, Baselines；
The Big Dipper signal is received as the signal flow of too many levels, and follow-up data processing concentrates on message processing module completion, works as data
The work of Baselines and the differentiation of fixed solution continuity is carried out after completion preparation.Wherein Baselines rudimentary algorithm be into
Ripe, technology fixed solution continuity is determined as the peculiar technology of the present invention.
Present invention additionally comprises Big Dipper high precision monitor type receiver, by rover station receiver and reference receiver in parallel
It is connected by serial ports with message processing module, carries out the computing of relative positioning and fixed solution continuity distinguished number, data storage
Module is connected with message processing module；It is inputted after Baseline solution storage after relative positioning and fixed solution continuity distinguished number computing
In data memory module.In view of relative positioning and fixed solution continuity distinguished number module of the hardware platform to the operation of input PC ends
Original observed quantity and the navigation message of known format are inputted, therefore Big Dipper signal is repeated no more in this specifications and receives related procedure,
It is main to focus on two aspects of Baselines and the differentiation of fixed solution continuity.
Baselines flow mainly includes data prediction, structure double difference observation, Detection of Cycle-slip, parameter Estimation, complete cycle
Ambiguity resolution；
The groundwork of data prediction is to provide the pseudorange that can be directly resolved after decoding and carrier phase and to benchmark
It is worth position and carries out initial value of the One-Point Location as parameter to be estimated；Then double difference observation is built, it can be effectively by difference
Eliminate the error related with satellite and receiver；Detection of Cycle-slip is carried out to measured value afterwards, it need to be by subsequent parameter when there is cycle slip
The initial value of estimation is reset；Parameter Estimation is carried out with completion Detection of Cycle-slip, then by the method for Extended Kalman filter
So as to obtain the float-solution of Kalman filtering state vector；Float-solution for state vector is, it is necessary to using suitable complete cycle mould
Paste degree processing strategy, is specially LAMBDA methods, to go back the true carrier phase between protosatellite and receiver, when being fixed
It can obtain accurate basic lineal vector solution after fuzziness afterwards.
The Baselines flow specifically includes following steps：
Step 1, data prediction decodes, One-Point Location, the initial value of parameter to be estimated including original observed quantity successively；It provides
The pseudorange and carrier phase and One-Point Location conduct is carried out to a reference value position that original observed quantity decoding can be resolved directly
The initial value of parameter to be estimated；
Step 2, double difference observation is built, includes difference between station successively, selects reference star, difference between star；It can be effective by difference
Ground eliminates the error related with satellite and receiver, resolves float-solution；
Step 3, Detection of Cycle-slip, detection include losing lock mark LLI and free-geometry linear combination；It need to be incited somebody to action when there is cycle slip follow-up
The initial value of parameter Estimation is reset；
Step 4, parameter Estimation, by the method for Kalman filtering come carry out parameter Estimation so as to obtain Kalman filtering state to
The float-solution of amount；
Step 5, Carrier Phase Ambiguity Resolution, for the float-solution of state vector, using LAMBDA methods also protosatellite and receiver it
Between true carrier phase, can obtain accurate basic lineal vector solution after fuzziness after obtaining fixed.
In the step 1, One-Point Location comprises the following steps, and is selected by satellite and explains to obtain satellite orbit with text
It calculates, then obtains correction amount calculating, then calculated by least square position and velocity calculated and RAIM.
In the step 2, structure double difference observation comprises the following steps, single poor between computer installation, selects reference star, composition
Double difference observational equation resolves float-solution.
In the step 3, Detection of Cycle-slip comprises the following steps, and starts, and travels through satellite, is traveled through if traveling through and completing next
Epoch；Judge whether, with multi-frequency observation amount, then to carry out free-geometry linear combination in this way if not traveling through and completing, such as otherwise
For losing lock judgement symbol；Judge whether cycle slip again, then reset Kalman filtering state vector in this way as initial value, such as otherwise return
Travel through the next satellite among satellite.
In the step 4, Kalman filtering comprises the following steps, and the time update of prediction is mutual with modified measurement updaue
For input and output；
（1）Time update includes：Step1 predicts next epoch state vector,
Step2 predicts next epoch error covariance square formation,
In formula、Valuation is initialized for 0 moment；
（2）Measurement updaue includes：Step1 calculates kalman gain matrix,
Step2 uses observationUpdate estimate,
Step3 updates error co-variance matrix,
In formulaFor input measurement value,For outputting measurement value.
In the step 5, LAMBDA methods comprise the following steps, and start, the processing of carrier wave measured value double difference, decisive equation number
Whether it is more than unknown number, then passes through integer ambiguity and the resolving of basic lineal vector float-solution and integer ambiguity and baseline in this way
Vectorial optimal solution resolves, until terminating；As otherwise returned to the processing of carrier wave measured value double difference.
In practical application, not all epoch can obtain fixed solution, calculation result is exported in chronological order, Gu
Surely solution state represents that float-solution state is represented with 2 with 1, then forms following initial solution state vector,
The present invention uses a kind of simple discriminant vector, passes throughIt is rolled up with initial solution state vector
Product obtains differentiating feature vector,
It is rightCodomain differentiated, select its value for 4 continuum as final fixed solution continuity region, i.e.,
It completes final fixed solution continuity and differentiates flow.
The invention has the advantages that proposing a kind of relatively simple fixed solution continuity method of discrimination, pass through simple convolution
Computing is i.e. recognizable and rejects isolated pseudo- fixed solution, effectively promotes Big Dipper high precision monitor receiver under the conditions of quasi-static
Deformation/displacement monitoring precision；The deployment of its algorithm is simple, is easy to apply, relatively low on the influence of fixed solution availability, possesses and preferably pushes away
Wide prospect.
Description of the drawings
Fig. 1 is Big Dipper high precision monitor type receiver hardware schematic.
Fig. 2 is Baselines schematic diagram.
Fig. 3 is Baselines flow chart.
Fig. 4 is One-Point Location flow chart.
Fig. 5 is structure double difference observation flow chart.
Fig. 6 is Detection of Cycle-slip flow.
Fig. 7 is LAMBDA method flow charts.
Fig. 8 is positioning result schematic diagram.
Fig. 9 is the root-mean-square error value figure of Fig. 8.
Figure 10 is differentiation feature vector value figure.
Figure 11 is the positioning result schematic diagram after changing.
Figure 12 is the root-mean-square error value figure of Figure 11.
Specific embodiment
A kind of fixed solution continuity method of discrimination disclosed in this invention may be directly applied to Big Dipper high-precision relative positioning
Or Static Precise Point Positioning, algorithm is realized simply, is not required to modify to receiver core algorithm, directly calculation result is grasped
Make, can effectively promote positioning accuracy of the Big Dipper high-precision receiver under quasi-static scene, have in the application of deformation monitoring class
There is vast market prospect.
1 to 12 the invention will be further described below in conjunction with the accompanying drawings, and the present invention is included with Big Dipper high-precision relative positioning
Based on algorithm, identified by simple convolution algorithm and show stationary state and the larger solution of actual error in position fixing process
It calculates as a result, convolution algorithm acts only on the fixed solution obtained using mature technology as a result, not being related to Baselines algorithm, is continuous
Property differentiate practical operation algorithm, drastically increase the displacement under the conditions of quasi-static in Big Dipper high precision monitor type receiver
Monitoring accuracy.
The Big Dipper high-precision relative localization algorithm includes three parts：The reception of Big Dipper signal, information processing, Baselines；
The Big Dipper signal is received as the signal flow of too many levels, and follow-up data processing concentrates on message processing module completion, works as data
The work of Baselines and the differentiation of fixed solution continuity is carried out after completion preparation.Wherein Baselines rudimentary algorithm be into
Ripe, technology fixed solution continuity is determined as the peculiar technology of the present invention.
Present invention additionally comprises Big Dipper high precision monitor type receivers, are received by rover station receiver 1 and base station in parallel
Machine 2 is connected by serial ports with message processing module 3, carries out the computing of relative positioning and fixed solution continuity distinguished number, data
Memory module 4 is connected with message processing module 3；Baseline solution storage after relative positioning and fixed solution continuity distinguished number computing
Afterwards in input data memory module 4.
In view of hardware platform inputs to the relative positioning and fixed solution continuity distinguished number module of the operation of input PC ends
Know original observed quantity and the navigation message of form, therefore Big Dipper signal is repeated no more in this specifications and receives related procedure, it is main poly-
Burnt Baselines and fixed solution continuity differentiate two aspects.
The basic lineal vector is described by the three-dimensional relative position between survey station, is resolved by simultaneous observation data difference
It arrives.Basic lineal vector is represented by the length of side and three-dimensional relative coordinate, as shown in Fig. 2, in order to intuitively reflect the opposite position of basic lineal vector
It puts feature, and combines actual needs, generally use topocentric coordinates represents the three-dimensional coordinate of basic lineal vector:。
Baselines flow mainly includes data prediction, structure double difference observation, Detection of Cycle-slip, parameter Estimation, complete cycle
Ambiguity resolution；
The groundwork of data prediction is to provide the pseudorange that can be directly resolved after decoding and carrier phase and to benchmark
It is worth position and carries out initial value of the One-Point Location as parameter to be estimated；Then double difference observation is built, it can be effectively by difference
Eliminate the error related with satellite and receiver；Detection of Cycle-slip is carried out to measured value afterwards, it need to be by subsequent parameter when there is cycle slip
The initial value of estimation is reset；Parameter Estimation is carried out with completion Detection of Cycle-slip, then by the method for Extended Kalman filter
So as to obtain the float-solution of Kalman filtering state vector；Float-solution for state vector is, it is necessary to using suitable complete cycle mould
Paste degree processing strategy, is specially LAMBDA methods, to go back the true carrier phase between protosatellite and receiver, when being fixed
It can obtain accurate basic lineal vector solution after fuzziness afterwards.
The Baselines flow specifically includes following steps：
Step 1, data prediction decodes, One-Point Location, the initial value of parameter to be estimated including original observed quantity successively；It provides
The pseudorange and carrier phase and One-Point Location conduct is carried out to a reference value position that original observed quantity decoding can be resolved directly
The initial value of parameter to be estimated；
Step 2, double difference observation is built, includes difference between station successively, selects reference star, difference between star；It can be effective by difference
Ground eliminates the error related with satellite and receiver, resolves float-solution；
Step 3, Detection of Cycle-slip, detection include losing lock mark LLI and free-geometry linear combination；It need to be incited somebody to action when there is cycle slip follow-up
The initial value of parameter Estimation is reset；
Step 4, parameter Estimation, by the method for Kalman filtering come carry out parameter Estimation so as to obtain Kalman filtering state to
The float-solution of amount；
Step 5, Carrier Phase Ambiguity Resolution, for the float-solution of state vector, using LAMBDA methods also protosatellite and receiver it
Between true carrier phase, can obtain accurate basic lineal vector solution after fuzziness after obtaining fixed.
In the step 1, One-Point Location comprises the following steps, and is selected by satellite and explains to obtain satellite orbit with text
It calculates, then obtains correction amount calculating, then calculated by least square position and velocity calculated and RAIM.
In the step 2, structure double difference observation comprises the following steps, single poor between computer installation, selects reference star, composition
Double difference observational equation resolves float-solution.
In the step 3, Detection of Cycle-slip comprises the following steps, and starts, and travels through satellite, is traveled through if traveling through and completing next
Epoch；Judge whether, with multi-frequency observation amount, then to carry out free-geometry linear combination in this way if not traveling through and completing, such as otherwise
For losing lock judgement symbol；Judge whether cycle slip again, then reset Kalman filtering state vector in this way as initial value, such as otherwise return
Travel through the next satellite among satellite.
In the step 4, Kalman filtering comprises the following steps, and the time update of prediction is mutual with modified measurement updaue
For input and output；
（1）Time update includes：Step1 predicts next epoch state vector,
Step2 predicts next epoch error covariance square formation,
In formula、Valuation is initialized for 0 moment；
（2）Measurement updaue includes：Step1 calculates kalman gain matrix,
Step2 uses observationUpdate estimate,
Step3 updates error co-variance matrix,
In formulaFor input measurement value,For outputting measurement value.
In the step 5, LAMBDA methods comprise the following steps, and start, the processing of carrier wave measured value double difference, and decisive equation number is
It is no to be more than unknown number, then resolved in this way by integer ambiguity and basic lineal vector float-solution and integer ambiguity and baseline to
It measures optimal solution to resolve, until terminating；As otherwise returned to the processing of carrier wave measured value double difference.
In practical application, not all epoch can obtain fixed solution, calculation result is exported in chronological order, Gu
Surely solution state represents that float-solution state is represented with 2 with 1, then forms following initial solution state vector,
The present invention uses a kind of simple discriminant vector, passes throughIt is rolled up with initial solution state vector
Product obtains differentiating feature vector,
It is rightCodomain differentiated, select its value for 4 continuum as final fixed solution continuity region, i.e.,
It completes final fixed solution continuity and differentiates flow.
Specific embodiment includes, the Big Dipper high accuracy positioning data surveyed using experimental site, totally 2844 observations, Gu
Surely 1687 are solved, accounts for 59.3%, before solution continuity differentiation is not fixed, positioning result schematic diagram is as shown in figure 8, the positioning
Result schematic diagram its abscissa is UTC time, and unit is the second, and the longitudinal axis is the deviation in tri- directions of ENU, unit m, due to this
The characteristics of inventing the quasi-static positioning scene of concern, the variation tendency of three curves should be gentle as far as possible, is significantly jumped in figure
Point is the point of pseudo- fixed solution, and the deterioration of calculation result precision is directly resulted in the appearance of up trip point.
Its ENU direction and plane compared with the root-mean-square error value with reference to true value as shown in figure 9, by taking E=0.0305m,
N=0.0632m, U=0.1027m obtain plane 0.1403m；Using the fixed solution continuity method of discrimination mentioned by the present invention, institute
Must differentiate that feature vector value is as shown in Figure 10, this feature vector value figure be its abscissa represent solution sequence number, from 1 to
2843, ordinate is the situation of change of feature vector value, and this feature value is from the state value for chronologically arranging Baseline solution（1
It is floating for fixation, 2）With discriminant vector I proposed by the present invention carry out convolution after as a result, value range be 4 to 8.
Differentiate feature according to it, reject and differentiate feature vectorMiddle value is not the pseudo- fixed solution in 4 correspondence period, altogether
5 epoch are rejected, only account for the 0.3% of total fixed skill.Positioning result schematic diagram after optimization is as shown in figure 11, the positioning result
Schematic diagram its abscissa is UTC time, and unit is the second, and the longitudinal axis is the deviation in tri- directions of ENU, unit m, due to the present invention
The characteristics of quasi-static positioning scene of concern, the variation tendency of three curves should be gentle as far as possible, by the differentiation of the present invention
After method examination, jump point amplitude is obviously reduced, and calculation result more tends to be steady, and precision significantly improves.Its ENU direction and plane
It is as shown in figure 12 compared with the root-mean-square error value with reference to true value, by taking E=0.0135mm, N=0.0093mm, U=0.0176m,
Obtain plane 0.0328m；Its positioning accuracy improves an order of magnitude before relatively optimizing, and achieves significant effect, and algorithm is realized
Simply, it is not required to modify to receiver core algorithm, directly calculation result is operated, it is high-precision can effectively promotes the Big Dipper
Positioning accuracy of the receiver under quasi-static scene is spent, prospect is had a vast market in the application of deformation monitoring class.
Claims (10)
1. based on the quasi-static scene positioning accuracy optimization method that fixed solution continuity differentiates, it is characterized in that,
Based on Big Dipper high-precision relative localization algorithm, identified and shown in position fixing process solid by simple convolution algorithm
Determine state and the larger calculation result of actual error, drastically increase in Big Dipper high precision monitor type receiver in quasi-static item
Displacement monitoring precision under part；
The Big Dipper high-precision relative localization algorithm includes three parts：The reception of Big Dipper signal, information processing, Baselines；
The Big Dipper signal is received as the signal flow of too many levels, and follow-up data processing concentrates on message processing module completion, when
The work that data carry out Baselines after completing preparation and fixed solution continuity differentiates.
2. the quasi-static scene positioning accuracy optimization method according to claim 1 differentiated based on fixed solution continuity,
It is characterized in, further includes Big Dipper high precision monitor type receiver, by rover station receiver in parallel（1）And reference receiver（2）
Pass through serial ports and message processing module（3）Connection, carries out the computing of relative positioning and fixed solution continuity distinguished number, and data are deposited
Store up module（4）With message processing module（3）Connection；Baseline solution after relative positioning and fixed solution continuity distinguished number computing is deposited
Input data memory module after storage（4）In.
3. the quasi-static scene positioning accuracy optimization method according to claim 1 differentiated based on fixed solution continuity,
It is characterized in,
Baselines flow mainly includes data prediction, structure double difference observation, Detection of Cycle-slip, parameter Estimation, integral circumference ambiguity
Degree resolves；
The groundwork of data prediction is to provide the pseudorange that can be directly resolved after decoding and carrier phase and to benchmark
It is worth position and carries out initial value of the One-Point Location as parameter to be estimated；Then double difference observation is built, it can be effectively by difference
Eliminate the error related with satellite and receiver；Detection of Cycle-slip is carried out to measured value afterwards, it need to be by subsequent parameter when there is cycle slip
The initial value of estimation is reset；Parameter Estimation is carried out with completion Detection of Cycle-slip, then by the method for Extended Kalman filter
So as to obtain the float-solution of Kalman filtering state vector；Float-solution for state vector is, it is necessary to using suitable complete cycle mould
Paste degree processing strategy, is specially LAMBDA methods, to go back the true carrier phase between protosatellite and receiver, when being fixed
It can obtain accurate basic lineal vector solution after fuzziness afterwards.
4. the quasi-static scene positioning accuracy optimization method according to claim 3 differentiated based on fixed solution continuity,
It is characterized in that Baselines flow specifically includes following steps：
Step 1, data prediction decodes, One-Point Location, the initial value of parameter to be estimated including original observed quantity successively；It provides
The pseudorange and carrier phase and One-Point Location conduct is carried out to a reference value position that original observed quantity decoding can be resolved directly
The initial value of parameter to be estimated；
Step 2, double difference observation is built, includes difference between station successively, selects reference star, difference between star；It can be effective by difference
Ground eliminates the error related with satellite and receiver, resolves float-solution；
Step 3, Detection of Cycle-slip, detection include losing lock mark LLI and free-geometry linear combination；It need to be incited somebody to action when there is cycle slip follow-up
The initial value of parameter Estimation is reset；
Step 4, parameter Estimation, by the method for Kalman filtering come carry out parameter Estimation so as to obtain Kalman filtering state to
The float-solution of amount；
Step 5, Carrier Phase Ambiguity Resolution, for the float-solution of state vector, using LAMBDA methods also protosatellite and receiver it
Between true carrier phase, can obtain accurate basic lineal vector solution after fuzziness after obtaining fixed.
5. the quasi-static scene positioning accuracy optimization method according to claim 4 differentiated based on fixed solution continuity,
It is characterized in：In the step 1, One-Point Location comprises the following steps, and is selected by satellite and explains to obtain satellite orbit meter with text
It calculates, then obtains correction amount calculating, then calculated by least square position and velocity calculated and RAIM.
6. the quasi-static scene positioning accuracy optimization method according to claim 4 differentiated based on fixed solution continuity,
It is characterized in：In the step 2, structure double difference observation comprises the following steps, single poor between computer installation, selects reference star, and composition is double
Poor observational equation resolves float-solution.
7. the quasi-static scene positioning accuracy optimization method according to claim 4 differentiated based on fixed solution continuity,
It is characterized in：In the step 3, Detection of Cycle-slip comprises the following steps, and starts, and travels through satellite, and next go through is traveled through if traveling through and completing
Member；Judge whether, with multi-frequency observation amount, then to carry out free-geometry linear combination in this way if not traveling through and completing, be such as otherwise
Losing lock judgement symbol；Judge whether cycle slip again, then reset Kalman filtering state vector in this way as initial value, such as otherwise return time
Go through the next satellite among satellite.
8. the quasi-static scene positioning accuracy optimization method according to claim 4 differentiated based on fixed solution continuity,
It is characterized in：In the step 4, Kalman filtering comprises the following steps, and the time update of prediction and modified measurement updaue are each other
Input and output；
（1）Time update includes：Step1 predicts next epoch state vector,
Step2 predicts next epoch error covariance square formation,
In formula、Valuation is initialized for 0 moment；
（2）Measurement updaue includes：Step1 calculates kalman gain matrix,
Step2 uses observationUpdate estimate,
Step3 updates error co-variance matrix,
In formulaFor input measurement value,For outputting measurement value.
9. the quasi-static scene positioning accuracy optimization method according to claim 4 differentiated based on fixed solution continuity,
It is characterized in：In the step 5, LAMBDA methods comprise the following steps, and start, the processing of carrier wave measured value double difference, and decisive equation number is
It is no to be more than unknown number, then resolved in this way by integer ambiguity and basic lineal vector float-solution and integer ambiguity and baseline to
It measures optimal solution to resolve, until terminating；As otherwise returned to the processing of carrier wave measured value double difference.
10. the quasi-static scene positioning accuracy optimization method according to claim 1 differentiated based on fixed solution continuity,
It is characterized in：
In practical application, not all epoch can obtain fixed solution, calculation result is exported in chronological order, fixed solution
State represents that float-solution state is represented with 2 with 1, then forms following initial solution state vector,
The present invention uses a kind of simple discriminant vector, passes throughIt is rolled up with initial solution state vector
Product obtains differentiating feature vector,
It is rightCodomain differentiated, select its value for 4 continuum as final fixed solution continuity region, i.e., it is complete
Differentiate flow into final fixed solution continuity.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111123303A (en) * | 2019-08-30 | 2020-05-08 | 广东星舆科技有限公司 | Method and device for acquiring positioning error data and processing method |
| CN115079232A (en) * | 2022-06-20 | 2022-09-20 | 成都新橙北斗智联有限公司 | Big Dipper high-precision positioning smooth solution data method based on ratio value |
| CN115480278A (en) * | 2022-08-01 | 2022-12-16 | 北方雷科(安徽)科技有限公司 | Dual-antenna directional robust algorithm suitable for complex multipath environment |
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