CN106792524B - A kind of mixing indoor orientation method based on dynamic environment bidirectional correcting - Google Patents
A kind of mixing indoor orientation method based on dynamic environment bidirectional correcting Download PDFInfo
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- CN106792524B CN106792524B CN201611138070.9A CN201611138070A CN106792524B CN 106792524 B CN106792524 B CN 106792524B CN 201611138070 A CN201611138070 A CN 201611138070A CN 106792524 B CN106792524 B CN 106792524B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
- H04W4/022—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences with dynamic range variability
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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Abstract
Indoor positioning technologies are widely used in many key areas, including indoor navigation, health care and mobile social networking etc..It is current to realize method there are mainly two types of indoor positionings: the fingerprint Map Method of the higher trilateration of error and higher cost.The latter has become prefered method because its accuracy is higher, but key factor is still unresolved: fingerprint map rejuvenation.And the present invention provides a kind of mixing indoor positioning system algorithm based on dynamic environment bidirectional correcting, which carries out bidirectional correcting using trilateration and fingerprint Map Method, to position tested point position in dynamic environment.This system extracts multiple characteristic values to inhibit multipath effect and improve positioning accuracy；By two kinds of localization method bidirectional correctings to adapt to dynamic environment and reduce the cost of repetition more new database.Performance by assessing this system is found: the system reduces positioning times, have better stability and versatility, substantially increase the precision positioned in dynamic environment.
Description
Technical field
The present invention relates to it is a kind of based in dynamic environment use trilateration and fingerprint Map Method mixed positioning algorithm,
Belong to wireless communication field, specially a kind of mixing indoor orientation method based on dynamic environment bidirectional correcting.
Background technique
Indoor locating system is indispensable in current popular application, such as indoor navigation, intelligent medical treatment, mobile social network
Network etc., according to investigations, economic benefit is up to 400,000,000 dollars in the global industry market of indoor positioning.The positioning of current main-stream is calculated
Method is broadly divided into two classes: being based on trilateration and fingerprint Map Method.Mainly pass through RSSI, TDOA, AOA based on trilateration
Etc. measuring distance or angle, then is resolved by distance and obtain unknown point coordinate.Mainly passed through based on fingerprint Map Method and is established in advance
The RSS information measured and fingerprint map are carried out pattern match, to obtain unknown by the fingerprint database of RSS and respective coordinates
Point coordinate.In comparison, inaccurate based on trilateration positioning accuracy, based on the enough height of fingerprint Map Method positioning accuracy, but
It is map rejuvenation higher cost.Therefore, a kind of precision is high and the proposition of indoor positioning algorithms at low cost is very urgent.
Summary of the invention
The present invention proposes to solve the problems, such as that existing indoor positioning algorithms positioning accuracy is not high, positioning cost is higher
A kind of mixing indoor orientation method based on dynamic environment bidirectional correcting.
The present invention adopts the following technical scheme that realization: a kind of mixing chamber based on dynamic environment bidirectional correcting is default
Position method, comprising the following steps:
(1) multiple characteristic values fingerprint map is established, there are two types of the combinations of specific features value: another one is the combination based on α value
Kind is the combination based on RSS value；
(2) step 1: carrying out RSS value sort descending to the RSS data of receiver input, first 4 data prediction: are chosen
RSS value, and the preferred receiver that its corresponding receiver ID is positioned as this；
Step 2: 4 receiver ID according to selection, only retain this corresponding data of 4 receivers, remaining receiver
Data all remove；
Step 3: calculating the Euclidean distance of each reference point in tested point and map by RSS value；
Step 4: carry out sort ascending to Euclidean distance, preceding 4 Euclidean distance values are chosen, and by its corresponding reference point
Preferred reference point as this positioning；
5th step carries out its corresponding data combination according to the 4 of selection preferred receivers and 4 preferred reference points
It exports respectively；
(3) weighting trilateration algorithm processing: step 1: arbitrarily choose three groups from four groups of data of output, according to connecing
The receipts machine coordinate α value being calculated and the RSS value chosen calculate measurement distance li, thereby determine that three groups of data respectively correspond
Weighting circle；
Step 2: calculating the intersecting point coordinate (x for acquiring three weighting circlesj,yj)；
Step 3: average current intersecting point coordinate acquires tested point and estimates position coordinates N indicates intersection point number；
Step 4: calculating the distance d that tested point estimates position and each intersection pointj,
Step 5: acquiring rough estimate position (xt,yt),
Step 6: if acquiring rough estimate position (xt,yt) weight circle intersection region in, then this point just as to
Reconnaissance；Otherwise, it removes the combination of these three circles and arbitrarily chooses three groups of carry out second steps in four groups of data again to the 6th step；
Step 7: it is average to reconnaissance, acquire position coordinates (x to be measuredT,yT)；
(4) it newly weights kNN algorithm process: solution being weighted to four groups of data of data prediction final output, is acquired
Position coordinates (x to be measuredFDB,yFDB), Wherein, (xm,ym) it is that data are located in advance
Manage the reference point coordinate acquired, DmIndicate the Euclidean distance of tested point and m-th of reference point；
(5) trilateration algorithm and the new position coordinates to be measured input threshold dector for weighting kNN algorithm and acquiring will be weighted
It carries out threshold test: calculating fiducial value:Compare error and threshold gamma, error
Then illustrate not meeting current positioning accuracy greater than threshold value, fingerprint map need to be returned and carry out map rejuvenation；Then show to accord with less than threshold value
Positioning accuracy is closed, positioning result (x is exportedn, yn):
It mixes indoor locating system and carries out bidirectional correcting using trilateration and fingerprint Map Method, thus in dynamic environment
Position tested point position.This system extracts multiple characteristic values to inhibit multipath effect and improve positioning accuracy；Pass through two kinds of positioning sides
Method bidirectional correcting is to adapt to dynamic environment and reduce the cost of repetition more new database.Performance by assessing this system is found:
The system reduces positioning times, have better stability and versatility, substantially increase the essence positioned in dynamic environment
Degree.
Detailed description of the invention
Fig. 1 is systems approach flow chart.
Fig. 2 is weighting trilateration geometry distribution map.
Specific embodiment
A kind of mixing indoor orientation method based on dynamic environment bidirectional correcting, comprising the following steps:
(1) establish multiple characteristic values fingerprint map, there are two types of the combinations of specific features value: one is the combinations based on α value (to receive
Machine ID number, respective coordinates, path loss coefficient η), another kind is that (RSS value, respective coordinates, correspondence connect the combination based on RSS value
Receipts machine ID number).
The acquisition methods of weight value α are as follows:
Wherein PL (d0) it is to join
Examination point d0The power loss at place, η are path loss coefficient,RiFor receiver SiIt measures and sends out
The distance of machine is penetrated,For the zero-mean gaussian stochastic variable being independently distributed.
Actual range R under NLOS environmenti, it is expressed as follows
Measurement distance l under LOS environmenti, it is expressed as followsUnder NLOS environment
Actual range RiWith the measurement distance l under LOS environmentiRelationship, be expressed as follows Ri=αili(4), wherein 0 < αi≤ 1, it is minimum
Weight α is expressed as followsWherein
Then loss factor η value is averagely acquired generally by multiple measurement RSS and d.Now for each in map
Grid is sought respectively using as characteristic value.Reference point of the acquiring method according to four vertex of grid, it is known that between reference point
Distance, a demand, which obtains between any two reference point RSS difference, repeatedly to ask flat after measurement in the hope of one of η value
, the η value of the grid, input database can be obtained.Its residual value can be measured directly by instrument.
(2) step 1: carrying out RSS value sort descending to the RSS data of receiver input, first 4 data prediction: are chosen
RSS value, and the preferred receiver that its corresponding receiver ID is positioned as this；Weighting trilateration at least needs 3
Receiver, precision can preferably be guaranteed and reduce error by choosing 4.
Step 2: 4 receiver ID according to selection, only retain this corresponding data of 4 receivers, remaining receiver
Data all remove；The information of to map carries out de-redundancy, to greatly reduce calculation amount, because each grid is set in map
Be set to rectangle, leave one's post anticipate the nearest points of tested point be rectangular mesh 4 vertex (reference point), and 4 points of selection exist
While progress reference point determines enough, the estimation of position can be more accurately carried out.
Step 3: calculating the Euclidean distance of each reference point in tested point and map；
Step 4: carry out sort ascending to Euclidean distance, preceding 4 Euclidean distance values are chosen, and by its corresponding reference point
Preferred reference point as this positioning；
5th step carries out its corresponding data combination according to the 4 of selection preferred receivers and 4 preferred reference points
It exporting respectively, when combination, the data that each receiver receives 4 preferred reference points simultaneously export one group of data, and four groups of final output
Data；
(3) weighting trilateration algorithm processing: step 1: arbitrarily choose three groups from four groups of data of output, according to connecing
The receipts machine coordinate α value being calculated and the RSS value selected calculate measurement distance li, thereby determine that three groups of data are right respectively
The weighting circle answered；
Step 2: calculating the intersecting point coordinate (x for acquiring three weighting circlesj,yj)；
Step 3: average current intersecting point coordinate acquires tested point and estimates position coordinates N indicates intersection point number；
Step 4: calculating the distance d that tested point estimates position and each intersection pointj,
Step 5: acquiring rough estimate position (xt,yt), pass through djCountdown square is asked as weighted value weighting intersecting point coordinate
,
Step 6: if acquiring rough estimate position (xt,yt) weight circle intersection region in, then this point just as to
Reconnaissance；Otherwise, it removes the combination of these three circles and arbitrarily chooses three groups of carry out second steps in four groups of data again to the 6th step；
Step 7: it is average to reconnaissance, acquire position coordinates (x to be measuredT,yT)；
(4) new weighting kNN algorithm process, the new kNN algorithm that weights includes kNN algorithm and weight, final to data prediction
Four groups of data of output are weighted solution, acquire position coordinates (x to be measuredFDB,yFDB),Wherein, (xm,ym) it is four head that data prediction acquires
Select reference point coordinate, DmIndicate the Euclidean distance of tested point and m-th of reference point；
(5) threshold test: the positioning result (position to be measured acquired of weighting trilateration algorithm and new weighting kNN algorithm
Coordinate) input threshold dector progress threshold test, steps are as follows: threshold value setting: threshold gamma is acquired by many experiments, and with
There are corresponding relationships for positioning accuracy.According to the required accuracy, corresponding threshold value is set.
Calculate fiducial value:
Threshold value comparison and output: comparing error and γ, then illustrates not meeting current positioning accuracy greater than threshold value, needs to return
Fingerprint map carries out map rejuvenation；Then show to meet positioning accuracy less than threshold value, export positioning result:
Bidirectional correcting
It is greater than threshold value based on threshold test, at least shows there is a kind of failure in two kinds of algorithms, fingerprint map need to be returned to RSS
Value and η value carry out data update.
Claims (1)
1. a kind of mixing indoor orientation method based on dynamic environment bidirectional correcting, it is characterised in that the following steps are included:
(1) multiple characteristic values fingerprint map is established, there are two types of the combinations of specific features value: one is the combination based on α value, another kind is
Combination based on RSS value；
(2) step 1: carrying out RSS value sort descending to the RSS data of receiver input, preceding 4 RSS data prediction: are chosen
Value, and the preferred receiver that its corresponding receiver ID is positioned as this；
Step 2: 4 receiver ID according to selection, only retain this corresponding data of 4 receivers, the data of remaining receiver
All removals；
Step 3: calculating the Euclidean distance of each reference point in tested point and map；
Step 4: carry out sort ascending to Euclidean distance, choose preceding 4 Euclidean distance values, and using its corresponding reference point as
The preferred reference point of this positioning；
5th step distinguishes its corresponding data combination according to the 4 of selection preferred receivers and 4 preferred reference points
Output, when combination, the data that each receiver receives 4 preferred reference points simultaneously export one group of data, four groups of data of final output；
(3) weighting trilateration algorithm processing: step 1: three groups are arbitrarily chosen from four groups of data of output, according to receiver
The α value that coordinate is calculated and the RSS value selected calculate measurement distance li, thereby determine that three groups of data are corresponding
Weighting circle；
Step 2: calculating the intersecting point coordinate (x for acquiring three weighting circlesj,yj)；
Step 3: average current intersecting point coordinate acquires tested point and estimates position coordinates N indicates intersection point number；
Step 4: calculating the distance d that tested point estimates position and each intersection pointj,
Step 5: acquiring rough estimate position (xt,yt),
Step 6: if acquiring rough estimate position (xt,yt) weighting in circle intersection region, then this point is just as to reconnaissance；
Otherwise, it removes the combination of these three circles and arbitrarily chooses three groups of carry out second steps in four groups of data again to the 6th step；
Step 7: it is average to reconnaissance, acquire position coordinates (x to be measuredT,yT)；
(4) it newly weights kNN algorithm process: solution being weighted to four groups of data of data prediction final output, is acquired to be measured
Position coordinates (xFDB,yFDB), Wherein, (xm,ym) it is that data prediction is asked
The reference point coordinate obtained, DmIndicate the Euclidean distance of tested point and m-th of reference point；
(5) the position coordinates to be measured input threshold dector that trilateration algorithm is acquired with new weighting kNN algorithm will be weighted to carry out
Threshold test: fiducial value is calculated:
Compare error and threshold gamma, error is greater than threshold value and then illustrates not meeting
Current positioning accuracy need to return to fingerprint map and carry out map rejuvenation；Then show to meet positioning accuracy, output positioning knot less than threshold value
Fruit (xn, yn):
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| CN106792524B (en) * | 2016-12-12 | 2019-11-22 | 太原理工大学 | A kind of mixing indoor orientation method based on dynamic environment bidirectional correcting |
| CN109059911B (en) * | 2018-07-31 | 2021-08-13 | 太原理工大学 | Data fusion method of GNSS, INS and barometer |
| US11150322B2 (en) | 2018-09-20 | 2021-10-19 | International Business Machines Corporation | Dynamic, cognitive hybrid method and system for indoor sensing and positioning |
| CN109640253B (en) * | 2018-12-26 | 2020-09-29 | 东阳市维创工业产品设计有限公司 | Mobile robot positioning method |
| CN111132012B (en) * | 2019-12-30 | 2021-04-09 | 京信通信系统（中国）有限公司 | Hybrid positioning method, system, computer equipment and storage medium |
| WO2022010340A1 (en) * | 2020-07-08 | 2022-01-13 | Mimos Berhad | A system and method for providing an indoor positioning tracking |
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| CN103901398A (en) * | 2014-04-16 | 2014-07-02 | 山东大学 | Position fingerprint positioning method based on combination ordering classification |
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