CN109444814A - A kind of indoor orientation method based on bluetooth and RFID fusion positioning - Google Patents
A kind of indoor orientation method based on bluetooth and RFID fusion positioning Download PDFInfo
- Publication number
- CN109444814A CN109444814A CN201811101460.8A CN201811101460A CN109444814A CN 109444814 A CN109444814 A CN 109444814A CN 201811101460 A CN201811101460 A CN 201811101460A CN 109444814 A CN109444814 A CN 109444814A
- Authority
- CN
- China
- Prior art keywords
- positioning
- bluetooth
- rfid
- indicate
- filter
- 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.)
- Pending
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
- 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
- G01S5/14—Determining absolute distances from a plurality of spaced points of known location
-
- 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
- G01S5/0257—Hybrid positioning
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a kind of indoor orientation methods positioned based on bluetooth and RFID fusion, it is related to indoor positioning technologies field, it is to provide that a kind of measurement accuracy is higher and system complexity is low the technical issues of solution, calculation amount is small, the high localization method of Fault Tolerance includes the following steps: that (1) builds bluetooth localizing environment；(2) bluetooth positioning is realized by improving weighted mass center algorithm；(3) RFID localizing environment is built；(4) RFID positioning is realized by LANDMARC algorithm；(5) by federated Kalman filtering method, bluetooth and RFID fusion positioning are realized.The present invention has the characteristics that positioning accuracy is high, software operand is low, anti-noise ability is strong, realizes requirement of real time under the premise of guaranteeing positioning accuracy.
Description
Technical field
The present invention relates to indoor positioning technologies fields, and in particular to a kind of interior based on bluetooth and RFID fusion positioning is fixed
Position method.
Background technique
With the rapid development of wireless network and mobile communication technology, demand of modern people's life to positioning is more invented
It is aobvious.The positioning of current social is more intended to the demand to indoor positioning, relative to outdoor positioning mature, indoor positioning without
It doubts also in the starting stage.The place of people's life leisure is increasingly intended to large-scale large-scale amusement and recreation shopping center；Greatly
The underground parking of type has begun prevalence；With the growth of population, comprehensive larger medical centers are also increasing.At this
In the case that a little large-scale indoor environments are grown rapidly, indoor positioning more highlights its importance.Pass through indoor positioning, a side
Special body location information can be known or be assigned in face；On the other hand it can monitor or the position of real-time tracking special body is believed
Breath.When disaster is come temporarily, indoor positioning can equally play very important effect, and such as in the case where fire occurs, interior is fixed
Position can provide interior space pattern complicated under the intensity of a fire for fire fighter, avoid unnecessary casualties.Indoor positioning
Using various fields are related to, this all expedites the emergence of fast forwarding through for this indoor positioning development.
Current common indoor positioning technologies mainly have ultrasonic technology, infrared technology, ultrafast band (UWB), radio frequency to know
Not (RFID), ZigBee, bluetooth positioning, magnetic orientation etc..
Ultrasonic wave positioning accuracy can achieve Centimeter Level, but ultrasonic attenuation is obvious, influence to position effective range.It is infrared
Line positioning accuracy is up to 5-10m.But infrared ray obstructs in transmission process vulnerable to object or wall and transmission range is shorter, positioning
System complexity is higher, and validity and practicability still have gap compared with other technologies.UWB positioning positioning accuracy does not surpass under normal conditions
15cm is crossed, but the bandwidth that UWB system occupies is very high, may interfere with other existing wireless communications.Zigbee positioning accuracy is reachable
Meter level establishes that accurate propagation model is extremely difficult due to being influenced by indoor complex environment, therefore the positioning of ZigBee technology
Precision is by biggish limitation.The positioning accuracy of ground magnetic orientation is better than 30m.Magnetic Sensor be the key that determine earth-magnetism navigation positioning because
Element, accurate environmental magnetic field reference map, reliable magnetic information matching algorithm are also highly important.Accurately Magnetic Sensor mistake
High cost hinders the widespread development of ground magnetic orientation.
Bluetooth location technology is suitable for measurement short distance, and power consumption is lower.Bluetooth equipment is small in size, is easily integrated into PDA, PC
And in mobile phone, therefore it is easy to popularize.It is integrated with the client of Bluetooth function mobile terminal device for holding, as long as equipment
Bluetooth function open, bluetooth indoor locating system can carry out position judgement to it.Indoor short distance is made using the technology
It is easy discovering device when positioning and signal transmission is not influenced by sighting distance.RFID indoor positioning is not necessarily to communication, and data transfer rate is high, peace
Quan Xinggao, it is readable ability and compressibility under the conditions of non-through, at low cost.
Chinese invention CN104105063A discloses a kind of monitoring location system and method based on RFID and blueteeth network,
Using RFID technique and the echo state network localization method based on RSSI model is used, traditional RSSI localization method is overcome to exist
Larger measurement error, the slow disadvantage of real-time response also solve existing monitoring probe and lack proprietary positioning system, position error
Greatly, in real time location difficulty the problems such as.This method is as a reference point by setting bluetooth nodes, and RFID label tag is moved into blueteeth network
Original state positions RFID by weight computing in real time, is not utilized respectively bluetooth positioning and RFID positioning is respective
Advantage, positioning accuracy do not have biggish promotion.
Chinese invention CN105527605A discloses a kind of multimodal fusion indoor orientation method, comprising steps of mixed to multimode
Unified identification and the transcoding for closing signal, obtain coarseness signal, and received by signal receiving end；Initial stage is carried out to RSSI coarse value
Under school；Using the mean value signal secondary correction method based on stacking-type set；It is calculated, is obtained using the modeling of triangle centroid algorithm
End position is received out.This method has merged WIFI, bluetooth and RFID positioning, carries out unified identification to multimode signal, secondly carries out
Initial stage calibration and secondary calibration, hardware integration is big, positions at high cost, computationally intensive, system complex.
Summary of the invention
In view of the deficiencies of the prior art, technical problem solved by the invention is to provide that a kind of measurement accuracy is higher and system
Complexity is low, and calculation amount is small, the high localization method of Fault Tolerance.
In order to solve the above technical problems, the technical solution adopted by the present invention is that a kind of merge positioning based on bluetooth and RFID
Indoor orientation method includes the following steps:
(1) bluetooth localizing environment is built
Area to be targeted is evenly dividing latticed, is evenly arranged Bluetooth base. station, arrangement m emits the bluetooth of Bluetooth signal
Node, position are denoted as (x respectively1,y1), (x2,y2) ..., (xm,ym)；
No matter indoors or outdoor, the distance of signal strength indication and receiving end and transmitting terminal that receiving end receives is at certain
Logarithm variation；For the distance between arbitrary receiving end and transmitting terminal, path loss are as follows:
The relationship of signal strength Yu distance measuring signal source distance is derived according to path loss:
It can obtain:
Wherein, PL (d0) it is reference distance d0The path loss at place, r are propagated loss index, and d is receiving end and hair
Penetrate the actual range at end, d0For reference distance, Pr (d) is receiving end away from the signal strength at transmitting terminal d, Pr (d0) it is receiving end
Away from transmitting terminal d0The signal strength at place；
(2) bluetooth positioning is realized by improving weighted mass center algorithm
For m collection point (x1,y1), (x2,y2) ..., (xm,ym), user's handheld configuration Beacon is equipped in positionIt is S to the signal strength that m position measures1,S2,...Sm, it is assumed that
Then weight are as follows:
It can must improve weighted mass center positioning result are as follows:
WhereinThe positioning coordinate positioned for user by bluetooth；
(3) RFID localizing environment is built
N reader is built in area to be targeted, has the number and u labels to be positioned of m reference location label, reads
Device is operated with the mode of continuous work；S indicates the signal strength vector between label to be positioned and corresponding reader,
θ indicates the signal strength vector between the corresponding reader of reference location label, in which:
S=(S1,S2,...,Sn)
SiIndicate the signal strength indication of the corresponding label to be positioned of i-th of reader, wherein (1, n) i ∈；
θ=(θ1,θ2,...,θn)
θiIndicate the signal strength indication of the corresponding reference label of i-th of reader, wherein (1, n) i ∈；
(4) RFID positioning is realized by LANDMARC algorithm
If EjFor the Euclidean distance of each label p to be positioned in j-th of reference label and localization region, positioning system is indicated
Relative intensity value between reference label and label to be positioned in system；
Calculate separately m reference label in localization region label to be positioned and localization region and Euclidean distance, be denoted as:
E=(E1,E2,...,Em)
By k nearest neighbor algorithm, the smallest k value in E is chosen, show that label to be positioned is sat by improving weighted mass center algorithm
Mark；
Wherein,For weight；
(5) by federated Kalman filtering method, bluetooth and RFID fusion positioning are realized
Assuming that the state equation and measurement equation of emerging system subfilter respectively indicate are as follows:
Xi(k+1)=φ (k) Xi(k)+W(k)
Zi(k)=Hi(k)Xi(k)+V (k),
Wherein, φ (k) and Hi(k) it respectively indicates the state-transition matrix of subfilter and measures transfer matrix；W (k) and V
(k) corresponding process noise and measurement noise are respectively indicated, the Gaussian Profile of Q (K) and R (k) that mean value is zero variance are obeyed,
And it is irrelevant；
The calculation process of federated Kalman filtering is as follows:
1. information is distributed
Information distribution is exactly to distribute system overall estimation value to each from filter according to certain information allocation rule
Among senior filter, distribution principle is as follows:
Xi=Xg
In above formula, βiThe distribution factor of system is represented, is metDistribution principle, Pi,PgIndicate corresponding
The covariance of system estimation error, Qi, QgIndicate corresponding white Gaussian noise, Xi, XgIndicate corresponding state vector；
2. the time updates
It is utilized respectively identical filtering algorithm and carries out time update in each subfilter and senior filter:
Pi(kk-1)=φ Pi(k-1)φT+Qi
WhereinIndicate that the k-1 moment estimates the prior state at k moment,Prolong after indicating k-1
State estimation, P indicate the covariance of corresponding system estimation error, and φ is state-transition matrix, QiIndicate white Gaussian noise；
3. the measurement of subfilter updates
There is no measurement in senior filter so not having to carry out measurement update, is measured more each from filter
New algorithm is as follows:
IfWherein,It is the transposition for measuring transfer matrix, P is indicated
The covariance of corresponding system estimation error, RiFor white Gaussian noise；
Pi(k)=(I-Ki(k)Hi(k))Pi(kk-1)
WhereinIndicate that the k-1 moment estimates the prior state at k moment, HiIt (k) is to measure transfer matrix, P table
Show the covariance of corresponding system estimation error, ZiIt (k) is observational equation；
4. global optimum merges
Senior filter is merged each from what filter exported according to certain mode, the following institute of blending algorithm
Show:
The state vector for wherein merging positioning system is to be set as X=[sx,vx,sy,vy]T, observation vector is Z [sx,sy]T
Wherein sx, syThe x-axis move distance and y-axis generated for object to be positioned by bluetooth positioning and RFID positioning moves
Distance, vx, vyFor object to be positioned x-axis and y-axis movement velocity；
First sub- sensor of system is bluetooth alignment sensor, and second sensor of system is RFID orientation sensing
Device, ifIt also is 0, senior filter is not involved in the distribution of global information；Filter is only carried out to each from filter
Information fusion；
The data anastomosing algorithm of subfilter is as follows:
Wherein, system overall situation estimated valueAnd PgIt is the state estimation that senior filter will be exported from subfilterWith
Covariance estimates PiMerged, process noise covariance battle array it is inverseTo indicate the information content of state equation；
In fusion Position Design, respectively using the position data that bluetooth positioning and RFID positioning generate as subfilter
Input, be filtered in subfilter respectively, generate local optimum estimated value, and as input senior filter into
The optimum fusion of row global information；Wherein, Kalman filtering algorithm is used in subfilter, passes through prediction, bearing calibration pair
Current location information is iterated update and correction, reduces error；Then, senior filter distributes optimum fusion value with information
Factor-beta1The initial value that bluetooth positioning subsystem filters next time as it is distributed to, with information distribution factor β2Distribute to RFID
Positioning subsystem is the initial value that it is filtered next time, so that the precision of part filter and global filtering be made to be improved, is realized
The optimum fusion of bluetooth and RFID.
Compared with prior art, beneficial effects of the present invention:
By carrying out bluetooth positioning using the weighted mass center location algorithm of logarithmic decrement model, reduces environment and positioning is tied
The influence of fruit improves bluetooth positioning accuracy.Some active labels are configured to reference label by LANDMARC, because it can be with
Tag signal strength information in detecting distance orientation is provided, the quantity of reader is reduced.It is reduced using LANDMARC algorithm
Positioning cost improves RFID positioning accuracy.Bluetooth is merged using federated Kalman filtering and RFID is positioned, and reduces system
Computation complexity, improves the fault-tolerant ability of system entirety, while improving positioning accuracy.The present invention have positioning accuracy it is high,
The feature that software operand is low, anti-noise ability is strong realizes requirement of real time under the premise of guaranteeing positioning accuracy.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is that bluetooth and RFID merge positioning system structure figure.
Specific embodiment
A specific embodiment of the invention is further described with reference to the accompanying drawing, but is not to limit of the invention
It is fixed.
Fig. 1 shows a kind of indoor orientation method based on bluetooth and RFID fusion positioning, includes the following steps:
(1) bluetooth localizing environment is built
Area to be targeted is evenly dividing latticed, is evenly arranged Bluetooth base. station, arrangement m emits the bluetooth of Bluetooth signal
Node, position are denoted as (x respectively1,y1), (x2,y2) ..., (xm,ym)；
No matter indoors or outdoor, the distance of signal strength indication and receiving end and transmitting terminal that receiving end receives is at certain
Logarithm variation；For the distance between arbitrary receiving end and transmitting terminal, path loss are as follows:
The relationship of signal strength Yu distance measuring signal source distance is derived according to path loss:
It can obtain:
Wherein, PL (d0) it is reference distance d0The path loss at place, r are propagated loss index, and d is receiving end and hair
Penetrate the actual range at end, d0For reference distance, Pr (d) is receiving end away from the signal strength at transmitting terminal d, Pr (d0) it is receiving end
Away from transmitting terminal d0The signal strength at place；
(2) bluetooth positioning is realized by improving weighted mass center algorithm
For m collection point (x1,y1), (x2,y2) ..., (xm,ym), user's handheld configuration Beacon is equipped in positionIt is S to the signal strength that m position measures1,S2,...Sm, it is assumed that
Then weight are as follows:
It can must improve weighted mass center positioning result are as follows:
WhereinThe positioning coordinate positioned for user by bluetooth；
(3) RFID localizing environment is built
N reader is built in area to be targeted, has the number and u labels to be positioned of m reference location label, reads
Device is operated with the mode of continuous work；S indicates the signal strength vector between label to be positioned and corresponding reader,
θ indicates the signal strength vector between the corresponding reader of reference location label, in which:
S=(S1,S2,...,Sn)
SiIndicate the signal strength indication of the corresponding label to be positioned of i-th of reader, wherein (1, n) i ∈,
θ=(θ1,θ2,...,θn)
θiIndicate the signal strength indication of the corresponding reference label of i-th of reader, wherein (1, n) i ∈；
(4) RFID positioning is realized by LANDMARC algorithm
If EjFor the Euclidean distance of each label p to be positioned in j-th of reference label and localization region, positioning system is indicated
Relative intensity value between reference label and label to be positioned in system；
Calculate separately m reference label in localization region label to be positioned and localization region and Euclidean distance, be denoted as:
E=(E1,E2,...,Em)
By k nearest neighbor algorithm, the smallest k value in E is chosen, show that label to be positioned is sat by improving weighted mass center algorithm
Mark；
Wherein,For weight；
(5) by federated Kalman filtering method, bluetooth and RFID fusion positioning are realized
Assuming that the state equation and measurement equation of emerging system subfilter respectively indicate are as follows:
Xi(k+1)=φ (k) Xi(k)+W(k)
Zi(k)=Hi(k)Xi(k)+V (k),
Wherein, φ (k) and Hi(k) it respectively indicates the state-transition matrix of subfilter and measures transfer matrix；W (k) and V
(k) corresponding process noise and measurement noise are respectively indicated, the Gaussian Profile of Q (K) and R (k) that mean value is zero variance are obeyed,
And it is irrelevant；
The calculation process of federated Kalman filtering is as follows:
1. information is distributed
Information distribution is exactly to distribute system overall estimation value to each from filter according to certain information allocation rule
Among senior filter, distribution principle is as follows:
Xi=Xg
In above formula, βiThe distribution factor of system is represented, is metDistribution principle, Pi,PgIndicate corresponding
The covariance of system estimation error, Qi, QgIndicate corresponding white Gaussian noise, Xi, XgIndicate corresponding state vector；
2. the time updates
It is utilized respectively identical filtering algorithm and carries out time update in each subfilter and senior filter:
Pi(kk-1)=φ Pi(k-1)φT+Qi
WhereinIndicate that the k-1 moment estimates the prior state at k moment,Prolong after indicating k-1
State estimation, P indicate the covariance of corresponding system estimation error, and φ is state-transition matrix, QiIndicate white Gaussian noise；
3. the measurement of subfilter updates
There is no measurement in senior filter so not having to carry out measurement update, is measured more each from filter
New algorithm is as follows:
IfWherein,It is the transposition for measuring transfer matrix, P is indicated
The covariance of corresponding system estimation error, RiFor white Gaussian noise；
Pi(k)=(I-Ki(k)Hi(k))Pi(kk-1)
WhereinIndicate that the k-1 moment estimates the prior state at k moment, HiIt (k) is to measure transfer matrix, P table
Show the covariance of corresponding system estimation error, ZiIt (k) is observational equation；
4. global optimum merges
Senior filter is merged each from what filter exported according to certain mode, the following institute of blending algorithm
Show:
The state vector for wherein merging positioning system is to be set as X=[sx,vx,sy,vy]T, observation vector is Z [sx,sy]T
Wherein sx, syThe x-axis move distance and y-axis generated for object to be positioned by bluetooth positioning and RFID positioning moves
Distance, vx, vyFor object to be positioned x-axis and y-axis movement velocity；
First sub- sensor of system is bluetooth alignment sensor, and second sensor of system is RFID orientation sensing
Device, if βm=0,It also is 0, senior filter is not involved in the distribution of global information；Filter only carries out letter from filter to each
Breath fusion；
The data anastomosing algorithm of subfilter is as follows:
Wherein, system overall situation estimated valueAnd PgIt is the state estimation that senior filter will be exported from subfilterWith
Covariance estimates PiMerged, process noise covariance battle array it is inverseTo indicate the information content of state equation；
Fig. 2 shows bluetooth and RFID to merge positioning system structure, in fusion Position Design, respectively with bluetooth positioning and
Input of the position data that RFID positioning generates as subfilter, is filtered in subfilter respectively, generates part
Optimal estimation value, and the optimum fusion of global information is carried out in senior filter as inputting.Wherein, it is adopted in subfilter
With Kalman filtering algorithm, update and correction are iterated to current location information by prediction, bearing calibration, reduces and misses
Difference.Then, information distribution factor is distributed to bluetooth positioning subsystem as it and filtered next time by optimum fusion value by senior filter
The initial value of wave, it is the initial value that it is filtered next time that information distribution factor, which is distributed to RFID positioning subsystem, to make office
The precision of portion's filtering and global filtering is improved, and realizes the optimum fusion of bluetooth and RFID.
Compared with prior art, beneficial effects of the present invention:
By carrying out bluetooth positioning using the weighted mass center location algorithm of logarithmic decrement model, reduces environment and positioning is tied
The influence of fruit improves bluetooth positioning accuracy.Some active labels are configured to reference label by LANDMARC, because it can be with
Tag signal strength information in detecting distance orientation is provided, the quantity of reader is reduced.It is reduced using LANDMARC algorithm
Positioning cost improves RFID positioning accuracy.Bluetooth is merged using federated Kalman filtering and RFID is positioned, and reduces system
Computation complexity, improves the fault-tolerant ability of system entirety, while improving positioning accuracy.The present invention have positioning accuracy it is high,
The feature that software operand is low, anti-noise ability is strong realizes requirement of real time under the premise of guaranteeing positioning accuracy.
Detailed description is made that embodiments of the present invention in conjunction with attached drawing above, but the present invention be not limited to it is described
Embodiment.To those skilled in the art, without departing from the principles and spirit of the present invention, to these implementations
Mode carries out various change, modification, replacement and variant are still fallen in protection scope of the present invention.
Claims (1)
1. a kind of indoor orientation method based on bluetooth and RFID fusion positioning, which comprises the steps of:
(1) bluetooth localizing environment is built
Area to be targeted is evenly dividing latticed, is evenly arranged Bluetooth base. station, arrangement m emits the bluetooth section of Bluetooth signal
Point, position are denoted as (x respectively1,y1), (x2,y2) ..., (xm,ym)；
No matter indoors or outdoor, the distance of signal strength indication and receiving end and transmitting terminal that receiving end receives is at certain logarithm
Variation；For the distance between arbitrary receiving end and transmitting terminal, path loss are as follows:
The relationship of signal strength Yu distance measuring signal source distance is derived according to path loss:
It can obtain:
Wherein, PL (d0) it is reference distance d0The path loss at place, r are propagated loss index, and d is receiving end and transmitting terminal
Actual range, d0For reference distance, Pr (d) is receiving end away from the signal strength at transmitting terminal d, Pr (d0) it is receiving end away from hair
Penetrate end d0The signal strength at place；
(2) bluetooth positioning is realized by improving weighted mass center algorithm
For m collection point (x1,y1), (x2,y2) ..., (xm,ym), user's handheld configuration Beacon is equipped in positionIt is right
The signal strength that m position measures is S1,S2,...Sm, it is assumed that
Then weight are as follows:
It can must improve weighted mass center positioning result are as follows:
WhereinThe positioning coordinate positioned for user by bluetooth；
(3) RFID localizing environment is built
Build n reader in area to be targeted, there is a number and u labels to be positioned of m reference location label, reader with
The mode of continuous work is operated；S indicates the signal strength vector between label to be positioned and corresponding reader, θ table
Show the signal strength vector between the corresponding reader of reference location label, in which:
S=(S1,S2,...,Sn)
SiIndicate the signal strength indication of the corresponding label to be positioned of i-th of reader, wherein (1, n) i ∈；
θ=(θ1,θ2,...,θn)
θiIndicate the signal strength indication of the corresponding reference label of i-th of reader, wherein (1, n) i ∈；
(4) RFID positioning is realized by LANDMARC algorithm
If EjFor the Euclidean distance of each label p to be positioned in j-th of reference label and localization region, indicate in positioning system
Relative intensity value between reference label and label to be positioned；
Calculate separately m reference label in localization region label to be positioned and localization region and Euclidean distance, be denoted as:
E=(E1,E2,...,Em)
By k nearest neighbor algorithm, the smallest k value in E is chosen, obtains tag coordinate to be positioned by improving weighted mass center algorithm；
Wherein,For weight；
(5) by federated Kalman filtering method, bluetooth and RFID fusion positioning are realized
Assuming that the state equation and measurement equation of emerging system subfilter respectively indicate are as follows:
Xi(k+1)=φ (k) Xi(k)+W(k)
Zi(k)=Hi(k)Xi(k)+V (k),
Wherein, φ (k) and Hi(k) it respectively indicates the state-transition matrix of subfilter and measures transfer matrix；W (k) and V (k) points
Corresponding process noise and measurement noise are not indicated, obey the Gaussian Profile of Q (K) and R (k) that mean value is zero variance, and mutually
It is uncorrelated；
The calculation process of federated Kalman filtering is as follows:
1. information is distributed
Information distribution is exactly to distribute system overall estimation value to each from filter and master according to certain information allocation rule
Among filter, distribution principle is as follows:
Xi=Xg
In above formula, βiThe distribution factor of system is represented, is metDistribution principle, Pi,PgIndicate corresponding system
Estimation error covariance, Qi, QgIndicate corresponding white Gaussian noise, Xi, XgIndicate corresponding state vector；
2. the time updates
It is utilized respectively identical filtering algorithm and carries out time update in each subfilter and senior filter:
Pi(k | k-1)=φ Pi(k-1)φT+Qi
WhereinIndicate that the k-1 moment estimates the prior state at k moment,Prolong state after indicating k-1
Estimation, P indicate the covariance of corresponding system estimation error, and φ is state-transition matrix, QiIndicate white Gaussian noise；
3. the measurement of subfilter updates
There is no measurement in senior filter so not having to carry out measurement update, carries out measuring update from filter each
Algorithm is as follows:
IfWherein,It is the transposition for measuring transfer matrix, P indicates corresponding
System estimation error covariance, RiFor white Gaussian noise；
Pi(k)=(I-Ki(k)Hi(k))Pi(k|k-1)
WhereinIndicate that the k-1 moment estimates the prior state at k moment, HiIt (k) is to measure transfer matrix, P indicates phase
The covariance for the system estimation error answered, ZiIt (k) is observational equation；
4. global optimum merges
Senior filter is merged each from what filter exported according to certain mode, and blending algorithm is as follows:
The state vector for wherein merging positioning system is to be set as X=[sx,vx,sy,vy]T, observation vector is Z [sx,sy]T
Wherein sx, syThe x-axis move distance and y-axis move distance that generate are positioned by bluetooth positioning and RFID for object to be positioned,
vx, vyFor object to be positioned x-axis and y-axis movement velocity；
First sub- sensor of system is bluetooth alignment sensor, and second sensor of system is RFID alignment sensor,
If βm=0,It also is 0, senior filter is not involved in the distribution of global information；Filter only carries out information from filter to each
Fusion；
The data anastomosing algorithm of subfilter is as follows:
Wherein, system overall situation estimated valueAnd PgIt is the state estimation that senior filter will be exported from subfilterAnd covariance
Estimate PiMerged, process noise covariance battle array it is inverseTo indicate the information content of state equation；
In fusion Position Design, respectively using the position data that bluetooth positioning and RFID positioning generate as the defeated of subfilter
Enter, be filtered in subfilter respectively, generates local optimum estimated value, and carry out in senior filter as input
The optimum fusion of global information；Wherein, Kalman filtering algorithm is used in subfilter, by prediction, bearing calibration to working as
Preceding location information is iterated update and correction, reduces error；Then, senior filter by optimum fusion value with information distribute because
Sub- β1The initial value that bluetooth positioning subsystem filters next time as it is distributed to, with information distribution factor β2It is fixed to distribute to RFID
Sub-systems are the initial value that it is filtered next time, so that the precision of part filter and global filtering be made to be improved, are realized blue
The optimum fusion of tooth and RFID.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811101460.8A CN109444814A (en) | 2018-09-20 | 2018-09-20 | A kind of indoor orientation method based on bluetooth and RFID fusion positioning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811101460.8A CN109444814A (en) | 2018-09-20 | 2018-09-20 | A kind of indoor orientation method based on bluetooth and RFID fusion positioning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109444814A true CN109444814A (en) | 2019-03-08 |
Family
ID=65530503
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811101460.8A Pending CN109444814A (en) | 2018-09-20 | 2018-09-20 | A kind of indoor orientation method based on bluetooth and RFID fusion positioning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109444814A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110176167A (en) * | 2019-05-31 | 2019-08-27 | 垂欧教科设备（上海）有限公司 | A kind of indoor intelligent teaching aid system and its operation method based on RFID |
| CN110320495A (en) * | 2019-08-01 | 2019-10-11 | 桂林电子科技大学 | A kind of indoor orientation method based on Wi-Fi, bluetooth and PDR fusion positioning |
| CN111132013A (en) * | 2019-12-30 | 2020-05-08 | 广东博智林机器人有限公司 | Indoor positioning method and device, storage medium and computer equipment |
| CN111257827A (en) * | 2020-01-16 | 2020-06-09 | 玉林师范学院 | High-precision non-line-of-sight tracking and positioning method |
| CN111970633A (en) * | 2020-08-24 | 2020-11-20 | 桂林电子科技大学 | Indoor positioning method based on WiFi, Bluetooth and pedestrian dead reckoning fusion |
| CN114237208A (en) * | 2021-08-12 | 2022-03-25 | 国网福建省电力有限公司 | Automatic path guiding method and system in unattended intelligent warehouse |
| CN114596943A (en) * | 2020-12-07 | 2022-06-07 | 韦氏（苏州）医疗科技有限公司 | Positioning and management and control system for medical equipment and personnel |
| CN118075686A (en) * | 2024-04-22 | 2024-05-24 | 南方电网调峰调频发电有限公司 | Intelligent sensing method, intelligent sensing device and intelligent sensing computer equipment for pumped storage foundation engineering target |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655561A (en) * | 2009-09-14 | 2010-02-24 | 南京莱斯信息技术股份有限公司 | Federated Kalman filtering-based method for fusing multilateration data and radar data |
| US20120326922A1 (en) * | 2011-06-27 | 2012-12-27 | Google Inc. | Gps and mems hybrid location-detection architecture |
| CN102928813A (en) * | 2012-10-19 | 2013-02-13 | 南京大学 | RSSI (Received Signal Strength Indicator) weighted centroid algorithm-based passive RFID (Radio Frequency Identification Device) label locating method |
| CN104105063A (en) * | 2014-07-28 | 2014-10-15 | 成都联星微电子有限公司 | Radio frequency identification device (RFID) and Bluetooth network based monitoring positioning system and method |
| CN104640076A (en) * | 2015-02-03 | 2015-05-20 | 南京邮电大学 | Indoor positioning method based on wireless signal data fusion |
| CN104837118A (en) * | 2015-04-29 | 2015-08-12 | 辽宁工业大学 | Indoor fusion positioning system and method based on WiFi and BLUETOOTH |
| CN105828435A (en) * | 2016-05-30 | 2016-08-03 | 天津大学 | Distance correction weighted centroid localization method based on reception signal intensity optimization |
| CN106686722A (en) * | 2017-01-23 | 2017-05-17 | 杭州电子科技大学 | Large-scale indoor environment positioning micro base station based on CSS (cascading style sheets) technology and operating method thereof |
| CN106879065A (en) * | 2015-12-11 | 2017-06-20 | 中南大学 | A kind of Wi-Fi indoor orientation methods based on improvement WKNN |
| CN107079257A (en) * | 2017-02-09 | 2017-08-18 | 深圳市汇顶科技股份有限公司 | Localization method and device based on bluetooth BLE |
| CN107144279A (en) * | 2017-04-28 | 2017-09-08 | 西安华宸导航通信有限公司 | Envirment factor dynamic calibrating method based on RSSI models in complex environment |
| US20170289951A1 (en) * | 2016-04-01 | 2017-10-05 | Saikat Dey | Geo-Localization Assembly and Methodology |
| CN107830862A (en) * | 2017-10-13 | 2018-03-23 | 桂林电子科技大学 | A kind of method of the indoor positioning pedestrian tracking based on smart mobile phone |
-
2018
- 2018-09-20 CN CN201811101460.8A patent/CN109444814A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655561A (en) * | 2009-09-14 | 2010-02-24 | 南京莱斯信息技术股份有限公司 | Federated Kalman filtering-based method for fusing multilateration data and radar data |
| US20120326922A1 (en) * | 2011-06-27 | 2012-12-27 | Google Inc. | Gps and mems hybrid location-detection architecture |
| CN102928813A (en) * | 2012-10-19 | 2013-02-13 | 南京大学 | RSSI (Received Signal Strength Indicator) weighted centroid algorithm-based passive RFID (Radio Frequency Identification Device) label locating method |
| CN104105063A (en) * | 2014-07-28 | 2014-10-15 | 成都联星微电子有限公司 | Radio frequency identification device (RFID) and Bluetooth network based monitoring positioning system and method |
| CN104640076A (en) * | 2015-02-03 | 2015-05-20 | 南京邮电大学 | Indoor positioning method based on wireless signal data fusion |
| CN104837118A (en) * | 2015-04-29 | 2015-08-12 | 辽宁工业大学 | Indoor fusion positioning system and method based on WiFi and BLUETOOTH |
| CN106879065A (en) * | 2015-12-11 | 2017-06-20 | 中南大学 | A kind of Wi-Fi indoor orientation methods based on improvement WKNN |
| US20170289951A1 (en) * | 2016-04-01 | 2017-10-05 | Saikat Dey | Geo-Localization Assembly and Methodology |
| CN105828435A (en) * | 2016-05-30 | 2016-08-03 | 天津大学 | Distance correction weighted centroid localization method based on reception signal intensity optimization |
| CN106686722A (en) * | 2017-01-23 | 2017-05-17 | 杭州电子科技大学 | Large-scale indoor environment positioning micro base station based on CSS (cascading style sheets) technology and operating method thereof |
| CN107079257A (en) * | 2017-02-09 | 2017-08-18 | 深圳市汇顶科技股份有限公司 | Localization method and device based on bluetooth BLE |
| CN107144279A (en) * | 2017-04-28 | 2017-09-08 | 西安华宸导航通信有限公司 | Envirment factor dynamic calibrating method based on RSSI models in complex environment |
| CN107830862A (en) * | 2017-10-13 | 2018-03-23 | 桂林电子科技大学 | A kind of method of the indoor positioning pedestrian tracking based on smart mobile phone |
Non-Patent Citations (5)
| Title |
|---|
| XINGBIN GE ET AL.: "Optimization wifi Indoor Positioning KNN algorithm Location-based Fingerprint", 《2016 7TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE(ICSESS)》 * |
| 李森等: "基于BLE与RFID的消防设施移动巡检***", 《消防科学与技术》 * |
| 纪敏: "WiFi\RFID室内融合定位方法的研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 * |
| 邓照群: "基于RFID的室内定位技术研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 * |
| 郭兆华等: "基于加权质心的蓝牙定位算法", 《数字技术与应用》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110176167A (en) * | 2019-05-31 | 2019-08-27 | 垂欧教科设备（上海）有限公司 | A kind of indoor intelligent teaching aid system and its operation method based on RFID |
| CN110176167B (en) * | 2019-05-31 | 2021-04-06 | 垂欧教科设备（上海）有限公司 | Indoor intelligent teaching aid system based on RFID and operation method thereof |
| CN110320495A (en) * | 2019-08-01 | 2019-10-11 | 桂林电子科技大学 | A kind of indoor orientation method based on Wi-Fi, bluetooth and PDR fusion positioning |
| CN111132013A (en) * | 2019-12-30 | 2020-05-08 | 广东博智林机器人有限公司 | Indoor positioning method and device, storage medium and computer equipment |
| CN111257827A (en) * | 2020-01-16 | 2020-06-09 | 玉林师范学院 | High-precision non-line-of-sight tracking and positioning method |
| CN111257827B (en) * | 2020-01-16 | 2023-07-14 | 玉林师范学院 | High-precision non-line-of-sight tracking and positioning method |
| CN111970633A (en) * | 2020-08-24 | 2020-11-20 | 桂林电子科技大学 | Indoor positioning method based on WiFi, Bluetooth and pedestrian dead reckoning fusion |
| CN114596943A (en) * | 2020-12-07 | 2022-06-07 | 韦氏（苏州）医疗科技有限公司 | Positioning and management and control system for medical equipment and personnel |
| CN114237208A (en) * | 2021-08-12 | 2022-03-25 | 国网福建省电力有限公司 | Automatic path guiding method and system in unattended intelligent warehouse |
| CN118075686A (en) * | 2024-04-22 | 2024-05-24 | 南方电网调峰调频发电有限公司 | Intelligent sensing method, intelligent sensing device and intelligent sensing computer equipment for pumped storage foundation engineering target |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109444814A (en) | A kind of indoor orientation method based on bluetooth and RFID fusion positioning | |
| Shu et al. | Gradient-based fingerprinting for indoor localization and tracking | |
| CN102209386B (en) | A kind of indoor wireless positioning method and device | |
| Deng et al. | Situation and development tendency of indoor positioning | |
| CN109195099A (en) | A kind of indoor orientation method merged based on iBeacon and PDR | |
| JP2013221943A (en) | Positioning method, device, and system | |
| Garcia et al. | Wireless Sensors self-location in an Indoor WLAN environment | |
| CN104507159A (en) | A method for hybrid indoor positioning based on WiFi (Wireless Fidelity) received signal strength | |
| CN112533163A (en) | Indoor positioning method based on NB-IoT (NB-IoT) improved fusion ultra-wideband and Bluetooth | |
| Yang et al. | Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application | |
| CN110007269A (en) | A kind of two stages wireless signal fingerprint positioning method based on Gaussian process | |
| Li et al. | An indoor positioning algorithm based on RSSI real-time correction | |
| Wang et al. | Integration of range-based and range-free localization algorithms in wireless sensor networks for mobile clouds | |
| KR20140102450A (en) | System for assuming position of base station and method for assuming position of base station thereof | |
| CN104965189A (en) | Indoor personnel positioning method based on maximum likelihood estimation | |
| Wu et al. | Cooperative motion parameter estimation using RSS measurements in robotic sensor networks | |
| Pu et al. | Fingerprint-based localization performance analysis: From the perspectives of signal measurement and positioning algorithm | |
| CN108732534A (en) | A kind of multi-tag Cooperative Localization Method based on weighting MDS | |
| Dardari et al. | A sub-optimal hierarchical maximum likelihood algorithm for collaborative localization in ad-hoc network | |
| CN104955148B (en) | A kind of wireless sensor network positioning method using electromagnetic wave symmetric propagation properties | |
| Zheng et al. | The study of RSSI in wireless sensor networks | |
| Srbinovska et al. | Localization techniques in wireless sensor networks using measurement of received signal strength indicator | |
| Jose et al. | Taylor series method in TDOA approach for indoor positioning system. | |
| Qing et al. | Wireless positioning method based on dynamic objective function under mixed LOS/NLOS conditions | |
| KR101459915B1 (en) | Method of Localization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190308 |