CN108307301A - Indoor orientation method based on RSSI rangings and track similitude - Google Patents
Indoor orientation method based on RSSI rangings and track similitude Download PDFInfo
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Classifications
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
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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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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
Abstract
The invention discloses one kind being based on RSSI (Received Signal Strength Indication, receive signal designation intensity) indoor orientation method of ranging and track similitude, first by Filtering Model process signal noise, and calculate the RSSI typical values of each group of base stations；Secondly tie-in equation group is established to the relationship between the distance of each base station according to wireless signal transmission theoretical model and point to be determined, and calculates the initial coordinate estimation of point to be determined；Finally estimated to search its close track by initial coordinate; it therefrom chooses and is drawn with the highest three close tracks of similarity of current track; the coordinates regional and initial coordinate estimation are combined the actual coordinate for calculating point to be determined by the coordinates regional for delimiting point to be determined according to this.Localization method in the present invention payes attention to current track and being associated between historical track, and most classical methods are independent process depending on each location Calculation.Method through the invention is established when the correlation model between prelocalization and history positioning, and is presently in position using the determination of this correlation model.
Description
Technical field
Industry field of the present invention is indoor positioning field, and it is relatively fixed and indoor to be suitable for furnishings layout
Article or the relatively stable indoor positioning application scenarios of the motion track of personnel；Technical field is related to technology of Internet of things, especially relates to
And a kind of indoor orientation method based on RSSI rangings and track similitude.
Background technology
The development of modern information technologies such as wireless communication, computer network and smart machine and gradually maturation are so that based on ground
The application generally existing of spatial position is managed, demand of the people to obtaining location information is increasingly urgent to.In recent years, the extensive of GPS answered
There is provided high-precision positioning service with for outdoor user, but due to blocked by above ground structure, furnishings article etc. it is objective because
The influence of element, indoor user can not obtain GPS signal, and GPS cannot already meet the location requirement of indoor user at this time.Interior is fixed
There is certain general character between the two as effective supplement to outdoor satellite navigator fix technology in position technology.But indoor positioning
Disturbing factor is more and its positioning accuracy request higher, these features make outdoor positioning method that can not directly apply to complicated ring
In the indoor positioning in border.Indoor orientation method is the key component of indoor positioning technologies, it has also become current indoor positioning field is ground
The hot spot studied carefully.
The indoor positioning technologies of mainstream mainly around raising indoor position accuracy, applicability and reduce use cost at present
Etc..According to localization method by means of communication, indoor positioning technologies can generally be divided into GNSS technologies, wireless location
The location technology and other location technologies of technology, GNSS and wireless location combination.And wireless location technology can be divided into it is infrared
Line positioning, ultrasonic wave positioning, WiFi positioning, bluetooth positioning etc..Wherein the wireless location technology based on bluetooth has technical costs
Cheap, the advantages that equipment volume is small, easy of integration universal, positioning accuracy is higher, can be used positioned in real time in object, cargo tracking,
The application scenarios such as robot navigation.
Indoor positioning technologies based on bluetooth are mainly using the polygon positioning mode based on RSSI rangings.This method
Basic principle is to resolve the position of anchor point at a distance from each Bluetooth base. station by measurement and positioning point.Positioning device receives each bluetooth
The wireless signal that base station is sent measures the RSSI of each Bluetooth base. station, and calculates it to respectively according to wireless signal transmission theoretical model
The air line distance of Bluetooth base. station.Utilize the position of polygon location algorithm and distance restraint computed position equipment.Although the positioning
Method introduces the Filtering Models such as mean filter, medium filtering, gaussian filtering and eliminates part signal noise, reduces nothing to a certain degree
Influence of the fluctuation of line signal to positioning accuracy, but this method excessively relies on the RSSI value of measured each Bluetooth base. station, changes
Yan Zhi, the measurement accuracy of RSSI value directly determine the precision of location algorithm.Indoor locating system during long-term use,
A large amount of user's history location data is had recorded, being associated between current location and historical location data is established, it is fixed to excavate history
The potential value of position data, to improve the precision of indoor positioning.
Invention content
It is an object of the invention to overcome the deficiencies in the prior art, propose a kind of based on RSSI rangings and track similitude
Indoor orientation method reduces proportions of the RSSI in location Calculation by being associated between introducing current track and historical track,
To eliminate influence of the part signal noise to positioning accuracy indirectly.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of indoor orientation method based on RSSI rangings and track similitude, including：
Step 1, each Bluetooth base. station RSSI data sets in present position are obtained, the signal corresponding to RSSI data according to acquisition
Transmitting base station is grouped RSSI data sets, i.e., the RSSI data of same base station are classified as one group.Since wireless signal loses journey
Degree is with reception apart from directly proportional, and the loss of signal is more, and the RSSI data of acquisition are often fewer, and positioning accuracy is lower.Therefore it needs
The number for analyzing the RSSI data that every group is acquired is typically chosen 4 to 8 more group data sets of RSSI value number and participates in positioning meter
It calculates.
Step 2, RSSI data sets optimization processing
Since Bluetooth base. station emits the influence that unstable, indoor article such as blocks at the objective factors, same position different time
The received RSSI value of section is also different, this leads to the size being worth between same group of RSSI data intensive data individuals, and there are larger
Difference, the number of the RSSI data between different groups is also not quite similar.In order to enhance the reliability of RSSI value, reduce as much as possible
Cause positioning result error excessive because of the fluctuation of signal, needs to be filtered every group of RSSI data set so that each group
The RSSI value that processing obtains more can really react positioning terminal to the distance of base station.The present invention using Gauss model with
The model treatment method that Kalman model is combined.
Step 2.1, Gauss model screens
It is usually according to wireless signal transmission theoretical model based on RSSI rangings
RSSI (d) in formuladBmThe signal strength values of signal receiving area when expression is d at a distance from signal generating source；RSSI
(d0)dBmIndicate to be d at a distance from signal generating source0When signal receiving area signal strength values, d0It is generally also referred to as reference distance,
Normal value is 1m in practical application；α is known as path loss index, is to influence related parameter with residing external environment, can pass through
Its value of survey calculation；β indicates extraneous enchancement factor, is to obey (0, σ2) Gaussian Profile stochastic variable.
From the signal strength instruction rssi known to above-mentioned analysis at a distance from the j of base station by being acquired when dj,dValue be obey (0,
σ2) Gaussian Profile stochastic variable, be denoted as：rssij,d~(0, σ2), probability density function is
Wherein rssij,d,kIndicate k-th of signal strength indication value at a distance from the base stations j by being acquired when d；It indicates
The mean value of the every group of RSSI data set acquired within the position data collecting period, calculation formula
N indicates the number of the rssi value selected by every group；σ indicates the standard deviation of every group of RSSI data set, calculation formula
Standard Normal Distribution
The basic thought of Gauss model filtering RSSI is the rssi value for choosing the confidence interval corresponding to the high confidence level,
I.e.
P(γ1≤rssij,d,k≤γ2)=1- δ
High confidence level δ generally takes 0.6 in practical engineering application, therefore can push over out the confidence interval [γ of confidence level 1- δ1,
γ2], i.e.,
δ indicates the probability value that the level of signifiance is smaller；Indicate that probability value isWhen critical value in normal distribution,
Its value can consult gaussian distribution table.It is then screened by Gauss model after the grouping of base station, the RSSI data sets of final every group of selection
Step 2.2, Kalman filtering smoothing processing
To further increase the stability of RSSI data, the data set screened to Gauss model using Kalman filtering is needed
It is smoothed, the basic thought of Kalman filtering is according to system model or theoretical empirical (in remover apparatus and environment
The RSSI of the interference of factor, the same base station that same position is received ought to be identical), the Posterior estimator based on k-1 states
rssij,d,k-1|k-1Obtain the k-state prior estimate rssi currently to be calculatedj,d,k|k-1, and combine the observation of current k-state RSSI
Value rssij,d,kPosterior estimator rssi is made to k-state RSSIj,d,k|k.Kalman filtering by description system mode process model
It is constituted with observation model two parts of observation system state.
Process model formalization representation
rssij,d,k|k-1=θ rssij,d,k-1|k-1+E1(k)
Wherein θ indicates systematic parameter, is 1 according to system model θ values；E1(k) indicate RSSI from k-state to k-1 states
White Gaussian noise in transition process or systematic error, obey (0, Q) Gaussian Profile, and Q is the variance of error.
Observation model formalization representation
rssi′j,d,k=ω rssij,d,k+E2(k)
Wherein rssi 'j,d,kIndicate the adjusted value of observation model；ω indicates systematic observation parameter, according to system model value
It is 1；E2(k) systematic observation white Gaussian noise or systematic observation error are indicated, (0, R) Gaussian Profile is obeyed, R is the side of error
Difference.
The Formal Representation of Kalman filtering
rssij,d,k|k=rssij,d,k|k-1+K(rssi′j,d,k-rssij,d,k|k-1)
Wherein K indicates kalman gain, calculation formula
Wherein Pk|k-1What is indicated is the error variance of k-state process model prior estimate, with the Posterior estimator of k-1 states
The relationship of error variance is Pk|k-1=Pk-1|k-1+ Q, parameter R, Q can be calculated by actual observation data statistics.
The data set G that Gauss model is screenedj={ rssij,d,l| l=1,2 ..., m } it is smooth using Kalman filtering
Processing obtains data set G 'j={ rssij,d,l|l| l=1,2 ..., m }, finally willFor calculating
Distance of the point to be determined to the base stations j.
Step 3, tie-in equation group is established by the projector distance equation of point to be determined to each base station location
Assuming that the initial coordinate that point to be determined is p is estimated as (xi′,yi'), step 2 is obtainedSubstitute into wireless signal
The distance that transmission theory model can obtain p points to base station j is dj.The phase of the located space residing for it is equivalent to by positioning terminal
Height is generally changed smaller, therefore parameter h can be set as by the relative height differential of positioning terminal and indoor base station0.It so far can be by remaining
String theorem obtains point to be determined to the projector distance equation of each base station location, and establishes following tie-in equation group：
Its midpoint (xj,yj) indicate base station j coordinate.
Step 4, Maximum Likelihood Estimation Method seeks equation group approximate solution
Under the ideal conditions without any loss of signal, equation group 1. in xi′,yi' existence and unique solution.But indoor environment is past
Toward also more complicated, furnishings article is more, wireless signal there are certain loss, and equation group 1. in equation it is general
It is 6 to 8, so cause equation group 1. without solution.The method that Maximum-likelihood estimation can be utilized thus seeks the approximate solution of equation group.
To further increase the precision of positioning result, for equation group 1. in equation using combination method, that is, from j equation
J-1 equation of middle selection is combined into new equation group, forms the new equation group of j kinds altogether.S-th new of equation group formal similarity
Wherein
The average value of all new solutions of equations of j kinds is finally asked to estimate as the initial coordinate of point to be determined：
Step 5, similar historical movement path is found
Interior space layout is often relatively fixed, during indoor article or personnel long-term movement indoors, positioning
Relatively-stationary historical movement path is formd, therefore often there is poles with certain historical movement paths for current motion track
Big similitude can improve the precision of the metastable indoor article of motion track or the positioning of personnel using this association.
It is implemented as follows：
Step 5.1, close track is searched
It concentrates and searches and initial position estimation (x from historical location datai′,yi') the closer point set P=of distance (x, y) | |
x-xi′|<λ,|y-yi′|<λ }, the value of parameter lambda depends on the requirement of positioning accuracy and indoor environment, general λ are set as 0.5m
To between 2m.Assuming that there are history positioning tracks
Indicate that positioning time, u show track respectivelyThe value of the number of node, u depends on interior space size, is usually set to 5
10) and current motion track toIfThen claimForClose track.
Step 5.2, the node in close track is calculatedNode corresponding with current track's
Distance,
By to distance sequence D=can be obtained after calculating<d1,d2,…,du>。
Step 5.3, similarity between calculating track
Grey relational grade analysis is according to the curve and the similar journey between the curve that reference sequences are formed for comparing sequence formation
It spends to determine the degree of association for comparing sequence and reference sequences, comparison curves is more similar to the geometry of reference curve, then it
Between association angle value it is bigger.Therefore the present invention is weighed current motion track and is gone through using the grey relational grade of distance sequence
Similitude between history motion track.Assuming that reference sequences are C=<c1,c2,…,cu>, then distance sequence D (comparing sequence) with ginseng
The computational methods for examining the grey relational grade between sequence C are as follows：
Step 5.3.1 calculates the absolute value of the difference of the corresponding element of distance sequence D and reference sequences C, i.e., one by one
| D-C |=| cv-dv|, v=1,2 ..., u
Step 5.3.2, determine min (| cv-dv|) and max (| cv-dv|)。
Step 5.3.3 calculates the degree of association coefficient of distance sequence D and each pair of corresponding elements of reference sequences C
Wherein η is referred to as resolution ratio, and η ∈ [0,1].The smaller expression resolution capability of η values is stronger, incidence coefficient ΦvBetween
Difference it is bigger, often take η=0.5 in actual application process.
Step 5.3.4 calculates the mean value of distance sequence D and the degree of association coefficient of reference sequences C corresponding elements
Wherein(also known as incidence coefficient) reflection is similar journey between distance sequence D and reference sequences C
Degree,Bigger, distance sequence D is more similar to reference sequences C.
Due to the element d in distance sequence DvIndicate the anchor point in current trackIt is corresponding with close track
PointDistance, therefore dvIt is more close to be worth smaller the two, works as dvTo be optimal when=0, at this timeTherefore sequence is referred to
Arrange C=<0,0,…0>.
Step 5.4, most like close track is found
Three tracks most like with current track are filtered out from numerous close tracks, i.e. incidence coefficient value is maximum
Three tracks (are set to), respective associated degree coefficientIt is right
The terminal of close track is answered to be respectively
Step 6, adjustment positioning
By the similitude of indoor article or personnel movement track it is found that the position of current point to be determined should be located at by most phase
As close track terminal where areas adjacent.Utilize the terminal of three most like close tracks Identified delta-shaped region and point to be determined
Initial coordinate estimation p (xi′,yi') it can determine the position of point to be determined.Method is as follows：
Step 6.1, triangle incenter O (x are calculatedo,yo) position
If
Then
Step 6.2, the initial coordinate of point to be determined is calculated to the line segment pO in the center of circle and the intersection point of triangle edges
IfLine is then judged respectively
Whether section pO and side AB, AC and BC have intersection point.Below to be illustrated for calculating the intersection point of line segment pO and line segment AB.
If determinant
Then pO and AB are overlapped or parallel.
If △ ≠ 0 calculates
If intersecting with AB there are 0≤λ≤1 and 0≤μ≤1, pO, intersection point (xi′+λ(xO-xi′),i′+λ(yO-yi')) i.e.
For the actual coordinate of point to be determined；If A, B, C three point on a straight line, the middle point coordinates for the longest line segment that is connected two-by-two is point to be determined
Actual coordinate；If p points are located in triangle, p point coordinates is the actual coordinate of point to be determined.
The present invention has the advantages that：
Indoor orientation method proposed by the present invention introduces being associated between current track and historical track, is associated with by establishing
Model improves the precision of the relatively stable indoor article or personnel positioning of motion track.
Invention is further described in detail with reference to the accompanying drawings and embodiments, but one kind of the present invention is surveyed based on RSSI
It is not limited to embodiment away from the indoor orientation method with track similitude.
Description of the drawings
Fig. 1 is the flow chart of indoor orientation method of the embodiment of the present invention based on RSSI rangings and motion track similitude；
Fig. 2 is office layout figure of the embodiment of the present invention；
Fig. 3 is the original RSSI data sets box traction substation of the embodiment of the present invention；
Fig. 4 is RSSI data set box traction substations after the processing of Filtering Model of the embodiment of the present invention.
Specific implementation mode
Shown in Fig. 1 to Fig. 4, the present embodiment is a kind of indoor positioning side based on RSSI rangings and track similitude
Method chooses indoor office room as application scenarios, selects the office of 11.8m × 8.9m as localization region, establish right angle seat
Mark system, wherein length direction are abscissa direction, and wide direction is ordinate direction.Arrange that eight numbers are X10001 in localization region
The Bluetooth base. station of~X10007, respective coordinates be respectively (- 2.4, -0.6), (- 6.8,0), (- 11.8, -0.8), (- 11.8, -
4.8), (- 9.7, -8.1), (- 2.4, -8.1), (- 0.6, -4.4).Localization region is now placed in by positioning device, according to this hair
Bright localization method is calculated.Essential implementation is as follows：
Step a is grouped the RSSI data sets of acquisition according to Bluetooth base. station, seven groups can be divided into the present embodiment, respectively
Group data distribution is referring to Fig. 3.
Step b handles every group of RSSI data set after grouping by Filtering Model, and gaussian filtering model eliminates part
Extreme noise screens relatively stable RSSI value, and Kalman filter model makes relatively stable RSSI data sets more they tend to
Smoothly, the combination of the two dramatically reduces signal noise, improves positioning accuracy.Each group RSSI data set Filtering Models
For data distribution that treated referring to Fig. 4, the RSSI typical values of final each Bluetooth base. station are as shown in table 1.
Table 1
| Bluetooth base. station | X10001 | X10002 | X10003 | X10004 | X10005 | X10006 | X10007 |
| RSSI value | -70 | -67 | -71 | -62 | -64 | -76 | -70 |
Step c, according to being associated between wireless signal transmission theoretical model and point to be determined and the range formula of each base station
Equation is established, and extracts six equatioies from this seven equatioies and establishes tie-in equation group, constitutes seven kinds of equation groups.With greatly seemingly
The method so estimated solves above-mentioned seven kinds of tie-in equation groups respectively, and each group equation group is acquired using the method for Maximum-likelihood estimation
For approximate solution referring to table 2, the mean value for calculating this seven kinds of tie-in equation group approximate solutions is (xi′,yi′)T=(6.66,4.03)T。
Table 2
| Equation group | First group | Second group | Third group | 4th group | 5th group | 6th group | 7th group | Mean value |
| xi,s′ | 6.93 | 6.51 | 6.64 | 6.69 | 6.70 | 6.51 | 6.96 | 6.66 |
| yi,s′ | 3.93 | 4.29 | 4.07 | 4.06 | 3.71 | 4.11 | 4.03 | 4.03 |
Step d is searched(since close track is more, therefore table 3 only enumerates similarity highest for all close tracks
Three, remaining will not enumerate), and calculate current track<(1.35,0.83),(2.12,1.57),(3.00,
2.12),(4.00,2.76),(4.85,3.67)>With the distance sequence of each close track.Because the application scenarios space is smaller,
So the node number n=5 of track.
Table 3
| Track | Node 1 | Node 2 | Node 3 | Node 4 | Node 5 | Terminal |
| Track 1 | (1.63,0.71) | (2.21,1.30) | (3.30,2.08) | (5.15,2.56) | (5.28,3.47) | (6.02,4.48) |
| Track 2 | (1.35,0.13) | (2.56,1.72) | (3.21,2.55) | (4.43,3.07) | (5.37,3.85) | (5.65,4.94) |
| Track 3 | (1.63,1.03) | (2.15,1.98) | (3.44,2.18) | (3.58,2.98) | (4.96,4.17) | (5.06,5.06) |
Step e calculates the similarity of current track and all close tracks, chooses the highest close rail of three similarities
Mark, and delimited by the delta-shaped region where positioning device physical location as final on trajectory.Track 1, track 2 and track 3 are phase
Like highest three close tracks are spent, distance sequence and similarity value are referring to table 4.Therefore the position by positioning device where practical
For by the terminal (6.02,4.48) of three most like close tracks, (5.65,4.94), triangle determined by (5.06,5.06)
On shape side.
Table 4
| Track | Distance sequence | Similarity |
| Track 1 | <0.305,0.270,0.302,1.167,0.474> | 0.844 |
| Track 2 | <0.700,0.381,0.479,0.530,0.554> | 0.844 |
| Track 3 | <0.692,0.414,0.444,0.474,0.512> | 0.901 |
Step f is estimated by the initial coordinate of positioning deviceIn conjunction with the delta-shaped region delimited by its close track
It can determine the position in actual coordinates by positioning device.Initial coordinate estimates (6.66,4.03) and triangle inscribed circle
The line in the center of circle (5.60,4.85) and the intersection point (5.92,4.60) of triangle edges are used as by positioning device in rectangular coordinate system
Position.
The foregoing is merely a prefered embodiment of the invention, is not intended to limit the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent replacement, improvement and so on should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of indoor orientation method based on RSSI rangings and track similitude, which is characterized in that including：
Step 1, each Bluetooth base. station RSSI data sets in present position are obtained, the signal transmitting corresponding to the RSSI data according to acquisition
Base station is grouped RSSI data sets；
Step 2, every group of RSSI data set after grouping is screened by gaussian filtering model, and passes through Kalman filtering mould
Type is smoothed, and the data set after smoothing processing is taken mean value；
Step 3, according to wireless signal transmission theoretical model calculate point to be determined to each base station distance；By point to be determined to respectively
The projector distance equation of base station location establishes tie-in equation group；
Step 4, the approximate solution of tie-in equation group is sought using Maximum Likelihood Estimation Method；
Step 5, according to obtained approximate solution search current track all close tracks, calculate current track in node with
The distance of corresponding node and the distance sequence of current track and each close track is calculated in close track；Calculate current track
With the similarity of all close tracks, the highest close track of three similarities is chosen, and quilt delimited by their final on trajectory
Delta-shaped region residing for positioning device physical location；
Step 6, the initial seat of delta-shaped region and point to be determined in conjunction with determined by the terminal of three most like close tracks
Mark estimation determines the position of point to be determined.
2. the indoor orientation method according to claim 1 based on RSSI rangings and track similitude, which is characterized in that right
Every group of RSSI data set after grouping is screened by gaussian filtering model, the RSSI data sets that group where the base stations j is chosen
Wherein, rssij,d,kIndicate k-th of signal strength indication value at a distance from the base stations j by being acquired when d；It indicates
The mean value of the every group of RSSI data set acquired in the position data collecting period；σ indicates the standard deviation of every group of RSSI data set；
N indicates the number of the rssi value selected by every group；δ indicates the probability value that the level of signifiance is smaller；Indicate that probability value isWhen
Critical value in normal distribution, value can consult gaussian distribution table.
3. the indoor orientation method according to claim 1 based on RSSI rangings and track similitude, which is characterized in that institute
Step 3 is stated, including：
Assuming that the initial coordinate that point to be determined is p is estimated as (xi′,yi'), the data set mean value that step 2 is obtainedSubstitute into nothing
The distance that line signal transmission theoretical model obtains p points to base station j is dj；
If being h by the relative height differential of positioning terminal and indoor base station0, point to be determined is obtained to each base station location by the cosine law
Projector distance equation, and establish following tie-in equation group：
Its midpoint (xj,yj) indicate base station j coordinate.
4. the indoor orientation method according to claim 3 based on RSSI rangings and track similitude, which is characterized in that institute
Step 4 is stated, including：
For equation group 1. in equation using combination method, select j-1 equation to be combined into new equation from j equation
Group forms the new equation group of j kinds altogether, and s-th new of equation group formal similarity is as follows：
Wherein
The average value of all new solutions of equations of j kinds is finally asked to estimate as the initial coordinate of point to be determined
。
5. the indoor orientation method according to claim 4 based on RSSI rangings and track similitude, which is characterized in that institute
Step 5 is stated, including：
Step 5.1, close track is searched
It concentrates and searches and initial position estimation (x from historical location datai′,yi') the closer point set P=of distance (x, y) | | x-
xi′|<λ,|y-yi′|<λ }, it is assumed that there are history positioning track
With current motion trackIfThen
ClaimForClose track；Wherein, requirement and indoor environment of the setting of parameter lambda depending on positioning accuracy,
Usually, required precision is higher or the interior space is narrower, and λ value is smaller；t1,…,tu+1The time of positioning is indicated respectively；U is indicated
TrackNode number, the value of u also depends on interior space size, and usually, the interior space is smaller, and u values are also got over
It is small；
Step 5.2, the node in close track is calculatedNode corresponding with current trackDistance
By to distance sequence D=can be obtained after calculating<d1,d2,…,du>。
Step 5.3, similarity between calculating track
The similitude between current motion track and historical movement path is weighed using the grey relational grade of distance sequence；Assuming that with reference to
Sequence is C=<c1,c2,…,cu>, then the computational methods of the grey relational grade between distance sequence D and reference sequences C are as follows：
Step 5.3.1 calculates the absolute value of the difference of the corresponding element of distance sequence D and reference sequences C one by one；
| D-C |=| cv-dv|, v=1,2 ..., u
Step 5.3.2, determine min (| cv-dv|) and max (| cv-dv|)；
Step 5.3.3 calculates the degree of association coefficient of distance sequence D and each pair of corresponding elements of reference sequences C
Wherein η is referred to as resolution ratio, and η ∈ [0,1]；
Step 5.3.4 calculates the mean value of distance sequence D and the degree of association coefficient of reference sequences C corresponding elements
Wherein,Incidence coefficientBigger, distance sequence D is more similar to reference sequences C.
Step 5.4, most like close track is found
Three tracks most like with current track, i.e. maximum three of incidence coefficient value are filtered out from numerous close tracks
Track is set toRespective associated degree coefficientIt is corresponding
The terminal of close track is respectively
6. the indoor orientation method according to claim 5 based on RSSI rangings and track similitude, which is characterized in that institute
Step 6 is stated, including：
Step 6.1, triangle incenter O (x are calculatedO,yO) position；
It enables
Then
Step 6.2, the initial coordinate of point to be determined is calculated to the line segment pO in the center of circle and the intersection point of triangle edges；
IfLine segment pO is then judged respectively
Whether there is intersection point with side AB, AC and BC；If there is intersection point, using intersection point as the actual coordinate of point to be determined；If A, 3 points of B, C is total
Line, the then actual coordinate of the middle point coordinates of the longest line segment of being connected two-by-two as point to be determined；If p points are located in triangle, p points
Actual coordinate of the coordinate as point to be determined.
7. the indoor orientation method according to claim 5 based on RSSI rangings and track similitude, which is characterized in that institute
It states parameter lambda and is set as 0.5m between 2m.
8. the indoor orientation method according to claim 5 based on RSSI rangings and track similitude, which is characterized in that institute
The value range for stating n is set as between 5 to 10.
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