CN106501828B - A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting - Google Patents
A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting Download PDFInfo
- Publication number
- CN106501828B CN106501828B CN201610850168.0A CN201610850168A CN106501828B CN 106501828 B CN106501828 B CN 106501828B CN 201610850168 A CN201610850168 A CN 201610850168A CN 106501828 B CN106501828 B CN 106501828B
- Authority
- CN
- China
- Prior art keywords
- satellite
- gdop
- receiver
- fuzzy
- fuzzy logic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computational Mathematics (AREA)
- Biomedical Technology (AREA)
- Fuzzy Systems (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Algebra (AREA)
- Artificial Intelligence (AREA)
- Automation & Control Theory (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a kind of high accuracy pseudo range one-point positioning methods based on fuzzy logic weighting, belong to Global Satellite Navigation System pseudorange One-Point Location field.Its steps includes obtaining satellite signal-to-noise ratio S by receiver and calculating the GDOP contribution degree of every satellite, and fuzzy logic algorithm is used based on this, the weight w of output satellite finally resolves the position coordinates of receiver, the positioning result of output receiver according to weighted least-squares method.The design method of the present invention program is flexibly effective, has good anti-multipath jamming performance, can improve the positioning performance of receiver, realize high-precision pseudorange One-Point Location.
Description
Technical field
The present invention relates to Global Satellite Navigation System pseudorange One-Point Location fields, especially a kind of to be weighted based on fuzzy logic
High accuracy pseudo range one-point positioning method.
Background technique
At present, pseudorange One-Point Location generallys use weighted least-squares localization method to improve positioning accuracy.Common side
Method has the method based on elevation of satellite, the method based on Satellite observation error variance.
Method based on elevation of satellite needs the elevation angle of real-time resolving satellite, then defends according to the adjustment of elevation angle size
Star participates in resolving the weight (satellite bigger to elevation angle gives bigger weight) when receiver location, and this method is certain
It can be improved positioning accuracy in degree, but in the environment of urban canyons, multipath serious interference, the big satellite of elevation angle can also
There can be multipath interference, its pseudorange accuracy does not ensure at this time, if giving big weight, can reduce final determine instead
Position precision.Therefore, elevation of satellite is only used to weight, and is limited in the improvement of positioning performance.
And it needs to solve Satellite observation error variance according to one piece of data based on the method for Satellite observation error variance
It calculates, then according to the setting reciprocal for carrying out weight of Satellite observation error variance, this method is not suitable for single epoch real-time resolving, only
It can be placed in the post-processing of One-Point Location and use, i.e., upper in application is also limited.
In view of the lower satellite of elevation angle, its signal-to-noise ratio is usually relatively low, and has passed through test proof: when generation multipath
When interference, the signal-to-noise ratio of satellite is generally also 4~12dB lower than normal level.Therefore, the method for weighting based on satellite signal-to-noise ratio exists
While having the advantages of weighting using elevation angle, also it can carry out certain benefit for inhibition multipath interference fringe.
The space geometry distribution of satellite also will affect positioning accuracy, therefore, in One-Point Location, it is necessary to take into account a title
For the error coefficient of geometric dilution of precision GDOP (geometric dilution of precision).Under normal circumstances, it receives
When the subtended angle of machine to each observation satellite line is all larger, the value of GDOP is smaller, and positioning accuracy is higher.Therefore every satellite is to GDOP
The percentage contribution of value is not quite similar, if the GDOP contribution degree of every satellite can be acquired, is weighted, can be mentioned to satellite according to this
High position precision.
Conventional method of weighting is using single argument (such as elevation angle) weighting method and bivariate (such as elevation angle and noise
Than) Result for Combinations method.Univariate weighting method has some limitations, and is easy to attend to one thing and lose sight of another；Linear group of bivariate
The specific gravity between bivariate can not be flexibly set by closing weighting method, it is difficult to reach ideal effect.However, multivariable is non-linear
Combined weighted method can overcome the shortcomings that both the above method, and the present invention realizes the non-thread of bivariate using fuzzy logic algorithm
Property combined weighted.
Fuzzy logic refers to the uncertain concept judgement for imitating human brain, reasoned thinking mode, for unknown-model or cannot
Determining description system, it is made inferences by means of subordinating degree function concept using fuzzy set and fuzzy rule, expresses transition
Property boundary or Qualitative Knowledge experience, simulate human brain mode, carry out fuzzy comprehensive estimation, reasoning solves what conventional method was difficult to tackle
Regular pattern composite fuzzy message problem.Therefore, it on the basis of having certain experiences, utilizes fuzzy logic to realize dual input variable
Nonlinear combination can neatly complete the weighting to satellite, compared with general LINEAR COMBINATION METHOD, it is easier to reach ideal effect
Fruit.
Summary of the invention
The purpose of the present invention is satellite signal-to-noise ratio in view of the deficiencies of the prior art, is utilized and considers satellite distribution feelings
Condition provides a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting.
Realize the technical solution of the object of the invention:
It is a kind of based on fuzzy logic weighting high accuracy pseudo range one-point positioning method each epoch has been required to
At following steps:
(1) to each satellite, satellite signal-to-noise ratio S and ephemeris are obtained by receiver；Position based on ephemeris computation satellite
It sets, and judges whether it is first epoch:
(1-1) then combines the elevation angle of the iteration calculation of initial value satellite of receiver location (herein if first epoch
The coordinate system for the position mentioned is WGS84 (ECEF-xyz))；If not first epoch then resolved in conjunction with a upper epoch
The receiver location arrived calculates the elevation angle of satellite；
(1-2) rejects the satellite that elevation angle is lower than set elevation mask；
(2) the GDOP contribution degree of every satellite is calculated；
(3) respectively to every satellite, with fuzzy logic algorithm, the weight w of output satellite；
(4) it is based on step (3) obtained weight w, the position coordinates of receiver are resolved according to weighted least-squares method, it is defeated
The positioning result of receiver out.
Preferably, elevation mask described in step (1) is 5~10 degree.It is more preferably 5 degree.
In above-mentioned technical proposal, the calculation method of the single satellite GDOP contribution degree of step (2): current all satellites are calculated
GDOP value, be denoted as gdop；Single satellite calculated is removed, the GDOP value of remaining satellite is calculated, is denoted as gdop'；The then list
The GDOP contribution degree of satellite is Δ G=gdop'-gdop；It repeats the above steps and obtains all single satellite GDOP contribution degrees.
In above-mentioned technical proposal, step (3) calculates the fuzzy logic algorithm of satellite weight w, detailed process are as follows:
(3-1) blurring: choosing the input parameter of satellite signal-to-noise ratio S and GDOP contribution degree Δ G as fuzzy logic algorithm,
X is used in the description of its ambiguous term respectively1And X2It indicates；Choose output parameter of the satellite weight w as fuzzy logic, ambiguous term
Description is indicated with Y；
X1, X2It is respectively indicated with the fuzzy set of Y as follows:
X1={ high, medium, low }
X2={ big, medium, small }
Y={ big, medium, small }
In above-mentioned fuzzy set, X1Indicate satellite signal-to-noise ratio, Linguistic Value AiFor high (height), medium (medium), low
(low)；X2Indicate satellite GDOP contribution degree, Linguistic Value BiFor big (big), medium (medium), small (small)；Y indicates satellite
Weight, Linguistic Value CiFor big (big), medium (medium), small (small)；
Common subordinating degree function has triangular function, normal function and trapezoidal function, and the present invention is all made of triangle person in servitude
Category degree function, its most important advantage are to calculate simply.
(3-2) fuzzy rule base: storing fuzzy rule in fuzzy rule base, fuzzy rule is the experience by largely testing
It summarizes, it is a kind of language Symbols form of the Intuitive inference of people.The effect of fuzzy rule base is will to input rise corresponding with output
Come.The form of fuzzy rule is
Ri:if X1 is Ai and X2 is Bi then Y is Ci
Wherein, RiIndicate i-th fuzzy rule, Ai, Bi, CiRespectively X1, X2, the corresponding Linguistic Value of Y；Due to of the invention
The reference input of fuzzy operation device is two-dimensional, and grade is 3 grades, so sharing 3 × 3=9 fuzzy rule.These are fuzzy
Rule is the foundation of On-line accoun.Fuzzy rule base is as shown in table 1:
1 fuzzy rule base of table
(3-3) fuzzy reasoning: the purpose of fuzzy reasoning is according to X1And X2Calculate Y.The mathematic(al) representation of fuzzy reasoning is
μ (Y)=min (μ (X1),μ(X2))
Wherein, μ (X1), μ (X2), μ (Y) is respectively X1, X2, the degree of membership of Y；
(3-4) ambiguity solution: utilizing fuzzy rule base, carries out fuzzy reasoning to the input quantity of blurring, completes by fuzzy quantity
To the conversion of precise volume, i.e. the weight w of output satellite.The present invention uses weighted mean method, and implementation method is as follows:
Wherein, CiIt is respectively " big " that is indicated, which is the state of Y, " mediun ", and when " small " gives the power of satellite distribution
Weight values, i.e.,
a1, a2, a3For weighted value to be set；
In above-mentioned technical proposal, the step (4) resolves the position of receiver, calculation method using weighted least-squares method
Are as follows:
Δ X=(HTWH)-1HTWΔρ
Wherein H indicates that satellite to the Direct cosine matrix of receiver, is obtained by conventional steps；n
For number of satellite, w is obtained by step (3)；Δ ρ indicates the pseudorange residuals vector of satellite, is obtained by conventional steps；Δ X=
[Δxk,Δyk,Δzk,Δδk] ' it is (xk,yk,zk,δk) ' iteration renewal amount, wherein (xk,yk,zk) it is receiver coordinate, k table
Show kth time iteration, δ indicates the receiver clock-offsets of the signal time of reception.
Enable xk+1=xk+Δxk, yk+1=yk+Δyk, zk+1=zk+Δzk, δk+1=δk+Δδk, seat in the plane is received according to so
Correcting mode is set, repeats least square resolving, until Δ xk, Δ yk, Δ zkReceiver that is sufficiently small, resolving at this time
Position coordinates be receiver positioning result.
Preferably, described sufficiently small to refer to
The utility model has the advantages that
The design method of the present invention program is flexibly effective, has good anti-multipath jamming performance, can improve receiver
Positioning performance, realize high-precision pseudorange One-Point Location.
Detailed description of the invention
Fig. 1: fuzzy logic algorithm block diagram.
Fig. 2: S subordinating degree function.
Fig. 3: the subordinating degree function of Δ G.
Fig. 4: the present invention is based on the error of the location algorithm of fuzzy logic weighting and common pseudorange One-Point Location algorithm is bent
Line comparison diagram.
Fig. 5: the present invention is based on the location algorithms of fuzzy logic weighting and the location algorithm based on elevation of satellite weighting
The error curve comparison diagram of horizontal location algorithm.
Specific embodiment
To facilitate the understanding of the present invention, a more comprehensive description of the invention is given in the following sections with reference to the relevant attached drawings.In attached drawing
Give presently preferred embodiments of the present invention.But the invention can be realized in many different forms, however it is not limited to this paper institute
The embodiment of description.On the contrary, purpose of providing these embodiments is keeps the understanding to the disclosure more thorough
Comprehensively.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Term as used herein in the specification of the present invention is intended merely to description tool
The purpose of the embodiment of body, it is not intended that in the limitation present invention.
The present embodiment describes the 2040th epoch, which shares eight satellites, be required to complete following steps:
(1) for each satellite, satellite signal-to-noise ratio S and ephemeris are obtained by receiver；Based on ephemeris computation satellite
Position, and judge whether it is first epoch: the present embodiment non-first epoch, then for a wherein satellite:
The receiver location that (1-1) was resolved in conjunction with a upper epoch, calculates the elevation angle of satellite；
(1-2) elevation angle is lower than/is more than or equal to the satellite of 5 ° of set elevation mask, and will be removed/retain (needs herein
It is noted that 5 ° of elevation mask be it is selected in the present embodiment, different elevation masks can also be set according to varying environment,
It is not construed as limitation of the present invention herein)；
Since first epoch has particularity, supplement is for example, in first epoch in the present embodiment: in conjunction with connecing
The initial position (tellurian any point, such as (0,0,0)) of receipts machine, calculates the elevation angle of satellite；
(2) by calculating, two satellites are rejected altogether, that is, the satellite for meeting elevation angle requirement shares six, calculates in six
The GDOP contribution degree of every satellite, by taking a wherein satellite as an example: passing through the side of conventionally calculation satellite geometry dilution of precision GDOP
Formula obtains gdop=1.9, by the same manner calculate remove this satellite after (five satellites) GDOP value, obtain gdop '=
2.2, then the GDOP contribution degree of the satellite is Δ G=gdop'-gdop=2.2-1.9=0.3；
(3) Fig. 1 is combined, respectively to every satellite, with fuzzy logic algorithm, the weight w of output satellite.
It is S=45dB with a wherein signal-to-noise ratio, for the satellite of GDOP contribution degree Δ G=0.3, fuzzy logic algorithm is asked
Steps are as follows for solution:
(3-1) determines input parameter (i.e. satellite signal-to-noise ratio X1With GDOP contribution degree X2), output parameter (satellite weight Y), mould
It is as follows to paste set expression:
X1={ high, medium, low }
X2={ big, medium, small }
Y={ big, medium, small }
1 fuzzy rule base of (3-2) table
(3-3) determines the fuzzy membership function of input parameter, such as Fig. 2.In fig. 2 it is possible to determine X1Respectively
" high ", " medium ", " low " and X2Respectively " big ", " medium ", degree of membership μ (X when " small "1) and μ (X2), such as
Shown in table 2 and table 3:
Table 2X1Degree of membership
| X1 | μ(X1) |
| high | 0.17 |
| mediun | 0.83 |
| low | 0.00 |
Table 3X2Degree of membership
| X2 | μ(X2) |
| big | 0.33 |
| mediun | 0.66 |
| small | 0.00 |
Determine fuzzy rule base such as table 1.Such as work as X1For " high ", X2For " big ", then Y is " big ", can according to table 2 and table 3
To obtain μ (Y)=min (0.17,0.33)=0.17, and so on, the value of nine groups of μ (Y) can be obtained；
(3-4) de-fuzzy.The value of μ (Y) obtained in (3-3) is used, then is enabledSo
After bring expression formula intoResult can be obtained:
The weight of all satellites found out is constituted into a weight battle array W (diagonal matrix):
Wherein, wnRepresent the weight of n-th satellite.
(4) it is based on step (3) obtained weight battle array W, the position coordinates of receiver are resolved according to weighted least-squares method,
The positioning result of output receiver.
Calculation method are as follows:
Δ X=(HTWH)-1HTWΔρ
Wherein H and Δ ρ are obtained by conventionally calculation mode.Based on this, the iteration renewal amount Δ of current epoch can be solved
X, then the positioning result of current epoch is xk+1=xk+Δxk, yk+1=yk+Δyk, zk+1=zk+Δzk.(calculation method such as technology
Shown in scheme, details are not described herein again)
By taking the calculating process of above-described embodiment as an example, the number of 24 hours IGS website gmsd on April 10th, 2016 is used
According to being tested, 30s is divided between epoch, the accurate coordinates of the website be (- 3607665.030086,4147868.086415,
3223717.257996).Error curve diagram is obtained as shown in Fig. 4 Fig. 5, in which:
In Fig. 4, by the location algorithm (grey parts) weighted based on fuzzy logic and common pseudorange One-Point Location algorithm
(black portions) are compared, the former horizontal positioning accuracy improves 30% or more, and elevation location precision improves 20% or more；
In Fig. 5, the location algorithm (grey parts) weighted based on fuzzy logic is determined with what is weighted based on elevation of satellite
Position algorithm (black portions) is compared, the former horizontal positioning accuracy improves 10% or more, and elevation location precision improves 5% left side
Right (specific value is shown in Table 4):
The positioning accuracy of 4 three kinds of algorithms of table compares
This location algorithm that above data illustrates that the present invention is mentioned is highly effective for the raising of positioning accuracy；Separately
Outside, the location algorithm based on fuzzy logic weighting also has preferable anti-multipath jamming performance, design method flexibly effective etc.
Advantage can improve the positioning performance of receiver, be suitable for quick, high-precision positioning occasion.
Claims (6)
1. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting, it is characterized in that for each epoch,
It needs to complete following steps:
(1) to each satellite, satellite signal-to-noise ratio S and ephemeris are obtained by receiver；Based on the position of ephemeris computation satellite, and
Judge whether it is first epoch:
(1-1) then combines the elevation angle of the iteration calculation of initial value satellite of receiver location if first epoch；If not first
A epoch, the then receiver location resolved in conjunction with a upper epoch calculate the elevation angle of satellite；
(1-2) rejects the satellite that elevation angle is lower than set elevation mask；
(2) the GDOP contribution degree of every satellite is calculated；
(3) respectively to every satellite, with fuzzy logic algorithm, the weight w of output satellite；Step (3) calculates satellite weight w's
Fuzzy logic algorithm, detailed process are as follows:
(3-1) blurring: the input parameter of satellite signal-to-noise ratio S and GDOP contribution degree Δ G as fuzzy logic algorithm, mould are chosen
X is used in paste term description respectively1And X2It indicates；Choose output parameter of the satellite weight w as fuzzy logic, ambiguous term description
It is indicated with Y；
X1, X2It is respectively indicated with the fuzzy set of Y as follows:
X1={ high, medium, low }
X2={ big, medium, small }
Y={ big, medium, small }
In above-mentioned fuzzy set, X1Indicate satellite signal-to-noise ratio, Linguistic Value AiFor high, medium, low；X2Indicate satellite GDOP
Contribution degree, Linguistic Value BiFor big, medium, small；Y indicates satellite weight, Linguistic Value CiFor big, medium,
small；
(3-2) fuzzy rule base: storing fuzzy rule in fuzzy rule base, the form of fuzzy rule is
Ri:if X1 is Ai and X2 is Bi then Y is Ci
Wherein, RiIndicate i-th fuzzy rule, Ai, Bi, CiRespectively X1, X2, the corresponding Linguistic Value of Y；Fuzzy rule base such as table 1
It is shown:
1 fuzzy rule base of table
(3-3) fuzzy reasoning: the mathematic(al) representation of fuzzy reasoning is
μ (Y)=min (μ (X1),μ(X2))
Wherein, μ (X1), μ (X2), μ (Y) is respectively X1, X2, the degree of membership of Y；
(3-4) ambiguity solution: using weighted mean method, and implementation method is as follows:
Wherein, CiIt is respectively " big " that is indicated, which is the state of Y, " mediun ", and when " small " gives the weighted value of satellite distribution,
I.e.
a1, a2, a3For weighted value to be set；
(4) it is based on step (3) obtained weight w, the position coordinates of receiver are resolved according to weighted least-squares method, output connects
The positioning result of receipts machine.
2. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting according to claim 1, feature
It is the calculation method of the single satellite GDOP contribution degree of step (2): calculates the GDOP value of current all satellites, be denoted as gdop；It removes
Single satellite calculated is gone, the GDOP value of remaining satellite is calculated, is denoted as gdop'；Then the GDOP contribution degree of the single satellite is
Δ G=gdop'-gdop；It repeats the above steps and obtains all single satellite GDOP contribution degrees.
3. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting according to claim 1, feature
It is that subordinating degree function is all made of Triangleshape grade of membership function.
4. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting according to claim 1, feature
It is the position that the step (4) resolve receiver using weighted least-squares method, calculation method are as follows:
Δ X=(HTWH)-1HTWΔρ
Wherein H indicates that satellite to the Direct cosine matrix of receiver, is obtained by conventional steps；N is to defend
Star number amount, w are obtained by step (3)；Δ ρ indicates the pseudorange residuals vector of satellite, is obtained by conventional steps；Δ X=[Δ
xk,Δyk,Δzk,Δδk] ' it is (xk,yk,zk,δk) ' iteration renewal amount, wherein (xk,yk,zk) it is receiver coordinate, k is indicated
Kth time iteration, δ indicate the receiver clock-offsets of the signal time of reception；
Enable xk+1=xk+Δxk, yk+1=yk+Δyk, zk+1=zk+Δzk, δk+1=δk+Δδk, repaired according to receiver location so
Positive way repeats least square resolving, until Δ xk, Δ yk, Δ zkIt is sufficiently small, the position of the receiver resolved at this time
Set the positioning result that coordinate is receiver, the Δ xk, Δ yk, Δ zkIt is sufficiently small, refer to
5. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting according to claim 1, feature
Be elevation mask described in step (1-2) be 5~10 degree.
6. a kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting according to claim 5, feature
Be elevation mask described in step (1-2) be 5 degree.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610850168.0A CN106501828B (en) | 2016-09-26 | 2016-09-26 | A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610850168.0A CN106501828B (en) | 2016-09-26 | 2016-09-26 | A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106501828A CN106501828A (en) | 2017-03-15 |
| CN106501828B true CN106501828B (en) | 2019-02-26 |
Family
ID=58290519
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610850168.0A Expired - Fee Related CN106501828B (en) | 2016-09-26 | 2016-09-26 | A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106501828B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107607969B (en) * | 2017-08-09 | 2021-01-05 | 东南大学 | Four-system pseudo range positioning method based on DCB correction |
| CN112731493B (en) * | 2018-10-08 | 2023-12-15 | 闽江学院 | Static pseudo-range single-point positioning method and device with high precision |
| CN109444930B (en) * | 2018-10-08 | 2020-11-10 | 闽江学院 | Single-point positioning method and device based on step-by-step weighted least square estimation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4928107A (en) * | 1988-06-22 | 1990-05-22 | Hitachi, Ltd. | Signal receiving method for a user's device in a global positioning system |
| CN102426374A (en) * | 2011-09-02 | 2012-04-25 | 东南大学 | GPS (Global Positioning System) mobile base station rapid positioning and resolving method |
| CN102608631A (en) * | 2011-10-28 | 2012-07-25 | 北京航空航天大学 | Self-adaption strong tracking unscented kalman filter (UKF) positioning filter algorithm based on fuzzy logic |
| CN102819029A (en) * | 2012-08-03 | 2012-12-12 | 浙江理工大学 | Supercompact combination satellite navigation receiver |
-
2016
- 2016-09-26 CN CN201610850168.0A patent/CN106501828B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4928107A (en) * | 1988-06-22 | 1990-05-22 | Hitachi, Ltd. | Signal receiving method for a user's device in a global positioning system |
| CN102426374A (en) * | 2011-09-02 | 2012-04-25 | 东南大学 | GPS (Global Positioning System) mobile base station rapid positioning and resolving method |
| CN102608631A (en) * | 2011-10-28 | 2012-07-25 | 北京航空航天大学 | Self-adaption strong tracking unscented kalman filter (UKF) positioning filter algorithm based on fuzzy logic |
| CN102819029A (en) * | 2012-08-03 | 2012-12-12 | 浙江理工大学 | Supercompact combination satellite navigation receiver |
Non-Patent Citations (2)
| Title |
|---|
| 一种改进的选星算法在GPS定位系统中的应用;田安红等;《海军工程大学学报》;20140430;第26卷(第2期);第45-48页 |
| 基于模糊算法的GPS定位误差估计;王美玲等;《中国惯性技术学报》;20070430;第15卷(第2期);第185-192页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106501828A (en) | 2017-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106501828B (en) | A kind of high accuracy pseudo range one-point positioning method based on fuzzy logic weighting | |
| CN106885576B (en) | AUV (autonomous Underwater vehicle) track deviation estimation method based on multipoint terrain matching positioning | |
| GB2547816A (en) | Actually-measured marine environment data assimilation method based on sequence recursive spare filtering three-dimensional variation | |
| CN103826298B (en) | Wireless sensor network positioning and computing method for collaborative iterative optimization | |
| CN106199662B (en) | A kind of Big Dipper high-precision locating method based on analytic hierarchy process (AHP) | |
| CN105785358B (en) | Radar target tracking method with Doppler measurement in direction cosine coordinate system | |
| CN106441279A (en) | Robot positioning method based on self-positioning and edge searching and system | |
| CN107153208A (en) | A kind of method of gps carrier phase cycle-slip detection and repair | |
| CN106482896A (en) | A kind of contactless factor of inertia discrimination method of arbitrary shape rolling satellite | |
| CN105021199B (en) | Multi-model self-adapting method for estimating state and system based on LS | |
| Tierra et al. | Planes coordinates transformation between PSAD56 to SIRGAS using a Multilayer Artificial Neural Network | |
| CN102162847A (en) | High-efficiency track correlation method based on singular value decomposition | |
| Sun et al. | Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons | |
| Tsou | Genetic algorithm for solving celestial navigation fix problems | |
| CN106597415A (en) | Method for improving error detection precision of sparse aperture imaging system under Gaussian noise | |
| Fan et al. | Fuzzy weighting approach for single point positioning with single-frequency pseudo-range observations | |
| CN102819000B (en) | Estimating method of overall magnetic moment of spacecraft | |
| CN116485857A (en) | High-time-resolution glacier thickness inversion method based on multi-source remote sensing data | |
| CN110058274A (en) | Time difference monitoring method and system between a kind of satellite navigation system | |
| CN113624242B (en) | Space debris orbit determination method, system, electronic equipment and storage medium | |
| Zhou et al. | Enhanced sequential nonlinear tracking filter with denoised pseudo measurements | |
| CN104391311A (en) | Satellite passive positioning method based on GPS broadcast data | |
| CN103954287B (en) | A kind of roadmap planning method of survey of deep space independent navigation | |
| Kaddour et al. | Fault detection and exclusion for GNSS measurements using observations projection on information space | |
| Yang et al. | The research on real-time map-matching algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190226 Termination date: 20210926 |