CN108398660A - A kind of terminal device localization method and system based on Wi-Fi cloud platform systems - Google Patents
A kind of terminal device localization method and system based on Wi-Fi cloud platform systems Download PDFInfo
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- CN108398660A CN108398660A CN201810015177.7A CN201810015177A CN108398660A CN 108398660 A CN108398660 A CN 108398660A CN 201810015177 A CN201810015177 A CN 201810015177A CN 108398660 A CN108398660 A CN 108398660A
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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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
Abstract
The invention discloses a kind of terminal device localization methods and system based on Wi Fi cloud platform systems.After intelligent robot, mobile job-oriented terminal equipment etc. are linked into AP access points by the method, terminal device is controlled uniformly by the realization of Wi Fi cloud platform systems, is accurately positioned.The present invention combines three angular distance calculating methods and location database matching method to obtain a kind of new terminal device localization method and system, concept i.e. according to comentropy in information theory determines entropy weight, the locator value that fusion obtains terminal device is carried out by entropy weight, and positioning result is obtained according to this, to improve the registration degree to terminal device.
Description
Technical field
The present invention relates to a kind of terminal device localization method and system based on Wi-Fi cloud platform systems, belongs to positioning skill
Art field.
Background technology
It is continued to develop instantly in information technology, the mark of networking, digitlization and the information sharing of all kinds of plateform systems
Standardization has become basic demand.In terms of substation's construction, have been able to be needed that power grid is supported to carry out automatic control according to business
The Premium Features such as system, intelligence adjusting, on-line analysis decision, coordination and interaction.Substation's terminal device especially intelligent robot
Introducing has then advanced optimized inspection flow, improves working efficiency.Meanwhile using cloud Wi-Fi cloud platform systems to intelligent machine
Device people carries out being uniformly accessed into management and monitoring so that the operation and management of substation is more convenient, also quickly advances power transformation
The unattended process stood.
Wi-Fi has gradually obtained extensive cognition by the advantage of itself and has approved as a kind of " network access mode ",
And have shown that out great application value and good development prospect.
By to the investigation of transformer station intelligent robot location requirement discovery, at present individually using triangulation calculation Furthest Neighbor or
It is that location database matching method is positioned, has larger position error for the positional accuracy of intelligent robot, even
Lead to Wrong localization.
Invention content
The technical problem to be solved by the present invention is in order to improve the registration degree to terminal device.The present invention proposes
A kind of terminal device localization method and system based on Wi-Fi cloud platform systems.
The present invention uses following technical scheme to solve above-mentioned technical problem：
The present invention proposes a kind of terminal device localization method based on Wi-Fi cloud platform systems, and the method is by triangle meter
Furthest Neighbor is calculated to be merged to obtain terminal device by entropy weight in Wi-Fi cloud platform systems with location database matching method
Locator value, step are：
(1) relative error of the triangulation calculation Furthest Neighbor and the location database matching method is normalized respectively；
(2) entropy of the triangulation calculation Furthest Neighbor and the relative error of the location database matching method is calculated separately；
(3) change of the relative error sequence of the triangulation calculation Furthest Neighbor and the location database matching method is calculated separately
Different degree coefficient；
(4) weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method is calculated separately；
(5) locator value is calculated according to the weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method.
Further, in method of the present invention, step (1) specifically includes：
(101) for the positional accuracy of anchor point investigation foundation, there are two indices sequence { Xt},{Yt, t=1,
2,…,N；
(102) t-th point of (X of note triangulation calculation Furthest Neighbor pairt, Yt) locator value be (Xit, Yit), remember location database
With t-th point of (X of method pairt, Yt) locator value be (Xjt, Yjt), then defining its error and being：
Wherein, δ is the error threshold of setting；
(103) errors are normalized, for the triangulation calculation Furthest Neighbor：
Wherein, q1tIt is the relative error of normalized triangulation calculation Furthest Neighbor,
For the location database matching method：
q2tIt is exactly the relative error of normalized location database matching method,
Further, in method of the present invention, in step (2), the entropy of the relative error of triangulation calculation Furthest Neighbor
Value h1Calculation formula be：
The entropy h of the relative error of location database matching method2Calculation formula be：
Wherein k=1/lnN, and have h1∈ [0,1], h2∈ [0,1].
Further, in method of the present invention, in step (3), the opposite of the triangulation calculation Furthest Neighbor is defined
The Variation factor d of error sequence1Calculation formula be：
d1=1-h1
Define the Variation factor d of the relative error sequence of the location database matching method2Calculation formula be：
d2=1-h2
D is acquired respectively with this1And d2Numerical value.
Further, in method of the present invention, for m kind methods, the calculation formula of weighting coefficient is：
Wherein, M=2 brings into and acquires l1, l2, l1Represent the weighting coefficient of triangulation calculation Furthest Neighbor, l2Represent position data
The weighting coefficient of storehouse matching method.
Further, in method of the present invention, for tested point (Xp, Yp), it is obtained according to the triangulation calculation Furthest Neighbor
The locator value gone out is (Xip, Yip), it is (X according to the locator value that the location database matching method obtainsjp, Yjp), then calculating
Go out final locator value：
Xop=l1Xip+l2Xjp
Yop=l1Yip+l2Yjp
(Xop, Yop) it is the locator value sought.
Further, in method of the present invention, the transmission of the Wi-Fi signal in the Wi-Fi cloud platforms system is damaged
Consumption model calculation formula be：
Pr (d)=K-10 ε lg (d/d0)(dBm)
K is constant, and the expression formula of K is：
Wherein, the actual range between d representation signals transmitting terminal and signal receiving end, ε are the loss factors of transmission space,
d0The short distance of initial reference between representation signal transmitting terminal and signal receiving end, 0.001W represent milliwatt, i.e. mW.
Further, in method of the present invention, the step of location database matching method, is：
(1) position feature library is trained：Rational choice reference by location node first guarantees to provide in detail for the actual location stage
Then thin location information receives the nothing of the wireless aps node of substation's arrangement in each reference mode using positioning terminal successively
Line signal records the signal pad value from three known different location AP points to it and corresponding MAC Address, is repeatedly taking
The average value and storage that pad value is sought after sample finally traverse each reference mode and obtain the data of desired position fingerprint recognition
Library；
(2) matching positioning：After Database, wirelessly connect what is received on tested point according to the matching algorithm chosen
Access point signals decay intensity is made comparisons with the data with existing in database, and the position (X of tested point is obtained by calculationj, Yj)。
Further, in method of the present invention, the matching positioning is using nearest neighbor algorithm, it is assumed that tested point
The RSS observations that (X, Y) is received are [ssl, ss2, ss3], and existing in database is recorded as (Xm,Ym)=[sslm,ss2m,
ss3m]；M ∈ [1, N T], N T are the record number in database, then nearest neighbor algorithm is expressed as following forms：
L=argminM ∈ [1, NT]=| | s-Sm||
By nearest neighbor algorithm, the position (X of tested point is matched from databasej, Yj),
Wherein, SmRepresentation vector [sslm,ss2m,ss3m], indicate existing record point (Xm,Ym) to the distance of three AP；S generations
Table vector [ssl, ss2, ss3], the distance of expression tested point (X, Y) to three AP.
The present invention also proposes a kind of terminal device positioning system based on Wi-Fi cloud platform systems, the system comprises：
Normalization computing module is missed for normalization triangulation calculation Furthest Neighbor respectively and the opposite of location database matching method
Difference；
Entropy computing module, for calculating separately triangulation calculation Furthest Neighbor and the relative error of location database matching method
Entropy；
Variation factor computing module, the phase for calculating separately triangulation calculation Furthest Neighbor and location database matching method
To the Variation factor of error sequence；
Weighting coefficient computing module, the weighting system for calculating separately triangulation calculation Furthest Neighbor and location database matching method
Number；
Locator value computing module is calculated for the weighting coefficient according to triangulation calculation Furthest Neighbor and location database matching method
Locator value.
The present invention has the following technical effects using above technical scheme is compared with the prior art：
Three angular distance calculating methods can be combined by the present invention with location database matching method, according to comentropy in information theory
Concept determine entropy weight, by entropy weight carry out fusion obtain terminal device locator value.Having the technical effect that of having improves pair
The registration degree of terminal device, effectively avoids the generation of larger position error and Wrong localization phenomenon.
Description of the drawings
Fig. 1 is robot triangulation location physical structure of the substation based on Wi-Fi cloud platform systems.
Fig. 2 is robot triangulation location geometrical principle figure of the substation based on Wi-Fi cloud platform systems.
Fig. 3 is the fusion positioning mode calculation flow chart based on Information Entropy.
Fig. 4 is the structural schematic diagram of the terminal device positioning system based on Wi-Fi cloud platform systems.
Specific implementation method
Technical scheme of the present invention is described in further detail below in conjunction with the accompanying drawings.Those skilled in the art of the present technique can
With understanding, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have and this hair
The identical meaning of general understanding of those of ordinary skill in bright fields.It should also be understood that in such as general dictionary
Those of definition term, which should be understood that, to be had a meaning that is consistent with the meaning in the context of the prior art, and unless picture
Here it equally defines, will not be explained with the meaning of idealization or too formal.
Embodiment one
The present embodiment provides the intelligent robot localization methods based on Wi-Fi cloud platform systems in a kind of, and this method can
It is realized in substation, algorithm flow chart is shown in Fig. 3, is broadly divided into three steps：
Step 1：Triangulation calculation Furthest Neighbor
(1) range measurement
Measurement point collects the RSS from three known different location nodes (Wi-Fi nodes) of substation at first, next is abided by
Follow the transmission loss model of Wi-Fi signal by using signal strength calculate measurement point to known Wi-Fi nodes approximate distance.
And Wi-Fi signal in the case of practical often by Path loss (path loss), Shadow fading (shadow fading)
Interference.Transmission loss model uses following simplified model：
Pr (d)=K-10 ε lg (d/d0)(dBm)
The expression formula of K is as follows：
Wherein, the actual range between d representation signals transmitting terminal and signal receiving end, ε are the loss factors of transmission space,
d0The short distance of initial reference between representation signal transmitting terminal and signal receiving end, 0.001W represent milliwatt, i.e. mW.
(2) distance calculates
Measurement point specific location is calculated by triangle location algorithm, principle is as shown in Figure 1.
It is measured at a distance from robot respectively by AP1, AP2, AP3, the position of robot is calculated according to distance.Its is several
What is abstracted as：Using three AP of known location in substation as the center of circle：A, 3 points of B, C.With signal attenuation model it is calculated away from
Three circles are talked about from for radius, finally obtain three round intersection points, as shown in Figure 2.
If the coordinate of measuring node D is (Xi, Yi), oneself knows A point coordinates (X1, Y1), the distance to D points is dl；B point coordinates
(X2, Y2), the distance to D points is d2；C point coordinates (X3, Y3), the distance to D points is d3.It is as follows equation group can be listed：
Solve the position (X for obtaining Di, Yi)。
Step 2：Location database matching method
(1) position feature library is trained
Main purpose is to build the database of location fingerprint identification.At design initial stage, Rational choice position is needed
Reference point guarantees to provide detailed location information for the actual location stage.Then positioning is utilized in each reference mode successively
Terminal receive substation arrangement wireless aps node wireless signal, record from three known different location AP points to its
Signal pad value and corresponding MAC Address seek the average value and storage of pad value after multiple sampling.Finally traverse each ginseng
It examines node and obtains the database of desired position fingerprint recognition.Due to being influenced by substation field environment, wireless signal strength is simultaneously
It is unstable, for the influence for overcoming RSS unstable to positioning, repeatedly measures and be averaged usually in each reference point.
(2) matching positioning
After Database, the wireless access point signal received on tested point is decayed according to the matching algorithm chosen
Intensity is made comparisons with the data with existing in database, and the position (X of tested point is obtained by calculationj, Yj).It is fixed in cloud Wi-Fi systems
Position matching is using nearest neighbor algorithm.It is assumed that the RSS observations that tested point receives are (Xj, Yj)=[ssl, ss2, ss3], number
It is recorded as (X according to existing in librarym, Ym)=[SSlm,SS2m,SS3m]；M ∈ [1, NT], NT are the record number in database.Then calculate
Method can be expressed as following forms：
L=argminM ∈ [1, NT]=| | s-Sm||
Wherein, SmRepresentation vector [sslm,ss2m,ss3m], indicate existing record point (Xm,Ym) to the distance of three AP；S generations
Table vector [ssl, ss2, ss3], indicate tested point (X, Y) to three AP distance, by nearest neighbor algorithm, from database
Allot the position (X of tested pointj, Yj)。
Step 3, from the angle of information theory, according to the error variation journey of step 1 and the described two methods of step 2
Degree selects Information Entropy to merge described two methods, algorithm flow chart is shown in Fig. 3 using the concept of comentropy.Based on entropy
The fusion and positioning method method calculating process of method is as follows：
To calculate entropy weight, N number of point is taken to be used as training dataset, the actual location value of N number of point is (X1, Y1), (X2,
Y2) ..., (XN, YN)；
It is (X according to the calculated locator value of triangulation calculation Furthest Neighbori1, Yi1), (Xi2, Yi2) ..., (XiN, YiN)；
It is (X according to the calculated locator value of location database matching methodj1, Yj1), (Xj2, Yj2) ..., (XjN, YjN)。
Step1. the relative error of triangulation calculation Furthest Neighbor and location database matching method is normalized respectively：
Investigating foundation for the positional accuracy of anchor point, there are two indices sequences
{Xt, t=1,2 ..., N }, { Yt, t=1,2 ..., N }, remember t-th point of (X of the first localization method pairt, Yt) determine
Place value is (Xit, Yit), then the error for defining the first localization method is：
δ is the error threshold of setting.
Errors are normalized, for the triangulation calculation Furthest Neighbor：
q1tIt is exactly normalized relative error,Similarly, note location database matching method pair the
T point (Xt, Yt) locator value be (Xjt, Yjt), the relative error q of the location database matching method can be acquired2t。
Step2. the entropy of triangulation calculation Furthest Neighbor and the relative error of location database matching method is calculated separately：
For the entropy h of the triangulation calculation Furthest Neighbor1Calculation formula be：
Wherein k=1/lnN, and have h1∈ [0,1].
Similarly, the entropy h of the location database matching method can be acquired2, h2∈ [0,1].
Step3. the change off course of the relative error sequence of triangulation calculation Furthest Neighbor and location database matching method is calculated separately
Spend coefficient:
According to the principle that the size of the entropy of a certain index of system and its degree of variation are inversely proportional, the triangulation calculation is defined
The Variation factor d of the relative error sequence of Furthest Neighbor1Calculation formula be：
d1=1-h1
Similarly, the Variation factor d of the location database matching method can be acquired2。
Step4. weighting coefficient is calculated：
The Variation factor of relative error sequence is inversely proportional with its weighting coefficient, for m kind methods, weighting coefficient
Calculation formula is:
Here M=2 is taken, brings into and acquires l1, l2。
Step5. locator value is calculated：
For tested point (Xp, Yp), for tested point (Xp, Yp), the locator value obtained according to triangulation calculation Furthest Neighbor is
(Xip, Yip), it is (X according to the locator value that location database matching method obtainsjp, Yjp), then locator value is calculated：
Xop=l1Xip+l2Xjp
Yop=l1Yip+l2Yjp
(Xop, Yop) it is the locator value sought.
Embodiment two
The present embodiment provides a kind of terminal device positioning system based on Wi-Fi cloud platform systems, structural schematic diagram is shown in
Fig. 4, for executing the method that above-described embodiment one is provided, which includes the system：
Computing module is normalized, normalizes the relative error of triangulation calculation Furthest Neighbor and location database matching method respectively；
Entropy computing module calculates separately the entropy of triangulation calculation Furthest Neighbor and the relative error of location database matching method
Value；
Variation factor computing module, calculates separately triangulation calculation Furthest Neighbor and the opposite of location database matching method is missed
The Variation factor of difference sequence；
Weighting coefficient computing module calculates separately the weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method；
Locator value computing module calculates positioning according to the weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method
Value.
Wherein, the calculation of each module is corresponding with the project in step 3 in above-described embodiment one, refers to above-mentioned reality
The associated description of step 3 in example one is applied, details are not described herein again.
The above is only some embodiments of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of terminal device localization method based on Wi-Fi cloud platform systems, which is characterized in that the method is by triangulation calculation
Furthest Neighbor is merged by entropy weight in Wi-Fi cloud platform systems to obtain determining for terminal device with location database matching method
Place value, step are：
(1) relative error of the triangulation calculation Furthest Neighbor and the location database matching method is normalized respectively；
(2) entropy of the triangulation calculation Furthest Neighbor and the relative error of the location database matching method is calculated separately；
(3) the change off course of the relative error sequence of the triangulation calculation Furthest Neighbor and the location database matching method is calculated separately
Spend coefficient；
(4) weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method is calculated separately；
(5) locator value is calculated according to the weighting coefficient of triangulation calculation Furthest Neighbor and location database matching method.
2. according to the method described in claim 1, it is characterized in that, step (1) specifically includes：
(101) for the positional accuracy of anchor point investigation foundation, there are two indices sequence { Xt},{Yt, t=1,2 ..., N；
(102) t-th point of (X of note triangulation calculation Furthest Neighbor pairt, Yt) locator value be (Xit, Yit), remember location database matching method
To t-th point of (Xt, Yt) locator value be (Xjt, Yjt), then defining its error and being：
Wherein, δ is the error threshold of setting；
(103) errors are normalized, for the triangulation calculation Furthest Neighbor：
Wherein, q1tIt is the relative error of normalized triangulation calculation Furthest Neighbor,
For the location database matching method：
q2tIt is exactly the relative error of normalized location database matching method,
3. according to the method described in claim 2, it is characterized in that, in step (2), the relative error of triangulation calculation Furthest Neighbor
Entropy h1Calculation formula be：
The entropy h of the relative error of location database matching method2Calculation formula be：
Wherein k=1/lnN, and have h1∈ [0,1], h2∈ [0,1].
4. according to the method described in claim 3, it is characterized in that, in step (3), the triangulation calculation Furthest Neighbor is defined
The Variation factor d of relative error sequence1Calculation formula be：
d1=1-h1
Define the Variation factor d of the relative error sequence of the location database matching method2Calculation formula be：
d2=1-h2
D is acquired respectively with this1And d2Numerical value.
5. according to the method described in claim 4, it is characterized in that, for m kind methods, the calculation formula of weighting coefficient is：
Wherein, M=2 brings into and acquires l1, l2, l1Represent the weighting coefficient of triangulation calculation Furthest Neighbor, l2Represent position data storehouse matching
The weighting coefficient of method.
6. according to the method described in claim 5, it is characterized in that, for tested point (Xp, Yp), according to the triangulation calculation away from
It is (X from the locator value that method obtainsip, Yip), it is (X according to the locator value that the location database matching method obtainsjp, Yjp), then
Final locator value is calculated：
Xop=l1Xip+l2Xjp
Yop=l1Yip+l2Yjp
(Xop, Yop) it is the locator value sought.
7. according to the method described in any one of claim 1 to 6 claim, which is characterized in that Wi-Fi cloud platforms system
The calculation formula of the transmission loss model of Wi-Fi signal in system is：
Pr (d)=K-10 ε lg (d/d0)(dBm)
K is constant, and the expression formula of K is：
Wherein, the actual range between d representation signals transmitting terminal and signal receiving end, ε are the loss factor of transmission space, d0Generation
The short distance of initial reference between table signal transmitting terminal and signal receiving end, 0.001W represent milliwatt, i.e. mW.
8. the method according to the description of claim 7 is characterized in that the step of location database matching method, is：
(1) position feature library is trained：Rational choice reference by location node first guarantees to provide for the actual location stage detailed
Then location information receives the wireless communication of the wireless aps node of substation's arrangement in each reference mode using positioning terminal successively
Number, the signal pad value from three known different location AP points to it and corresponding MAC Address are recorded, after multiple sampling
The average value and storage for seeking pad value finally traverse each reference mode and obtain the database of desired position fingerprint recognition；
(2) matching positioning：After Database, according to the wireless access point that will be received on tested point of matching algorithm chosen
Signal decay intensity is made comparisons with the data with existing in database, and the position (X of tested point is obtained by calculationj, Yj)。
9. according to the method described in claim 8, it is characterized in that, matching positioning is using nearest neighbor algorithm, it is assumed that
The RSS observations that tested point (X, Y) receives are [ssl, ss2, ss3], and existing in database is recorded as (Xm,Ym)=[sslm, ss2m,ss3m]；M ∈ [1, N T], N T are the record number in database, then nearest neighbor algorithm is expressed as following forms：
L=argminM ∈ [1, NT]=| | s-Sm||
By nearest neighbor algorithm, the position (X of tested point is matched from databasej, Yj),
Wherein, SmRepresentation vector [sslm,ss2m,ss3m], indicate existing record point (Xm,Ym) to the distance of three AP；Behalf to
It measures [ssl, ss2, ss3], the distance of expression tested point (X, Y) to three AP.
10. a kind of terminal device positioning system based on Wi-Fi cloud platform systems, which is characterized in that the system comprises：
Computing module is normalized, the relative error for normalizing triangulation calculation Furthest Neighbor and location database matching method respectively；
Entropy computing module, the entropy for calculating separately triangulation calculation Furthest Neighbor and the relative error of location database matching method
Value；
Variation factor computing module is missed for calculating separately triangulation calculation Furthest Neighbor and the opposite of location database matching method
The Variation factor of difference sequence；
Weighting coefficient computing module, the weighting coefficient for calculating separately triangulation calculation Furthest Neighbor and location database matching method；
Locator value computing module calculates positioning for the weighting coefficient according to triangulation calculation Furthest Neighbor and location database matching method
Value.
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| CN111885488B (en) * | 2020-07-24 | 2023-03-03 | 南京快轮智能科技有限公司 | Electric scooter central control system and positioning method |
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