CN109462820A - It is a kind of for tracking the RSSI approximating method of low speed move vehicle - Google Patents
It is a kind of for tracking the RSSI approximating method of low speed move vehicle Download PDFInfo
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- CN109462820A CN109462820A CN201811368105.7A CN201811368105A CN109462820A CN 109462820 A CN109462820 A CN 109462820A CN 201811368105 A CN201811368105 A CN 201811368105A CN 109462820 A CN109462820 A CN 109462820A
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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
- H04W4/029—Location-based management or tracking services
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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
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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Abstract
The present invention discloses a kind of for tracking the RSSI approximating method of low speed move vehicle, comprising: arranges WIFI sniff equipment in conplane both sides of the road, establishes the relative coordinate system of tracing-positioning system；Gaussian filtering processing is carried out to the RSSI signal that WIFI sniff equipment receives, signal damped expoential λ value in attenuation model is determined, obtains the signal attenuation model for meeting real road environment；Judgement screening is carried out to RSSI value, by the data after judgement screening, Kalman filter processing is carried out, obtains effective RSSI signal.The present invention obtains the radio signal attenuation model for meeting real road environment with Gaussian filtering, and considers the changing rule of RSSI value under low speed move vehicle scene, introduces worst error distance criterion and is screened, finally carries out Kalman filter processing.The present invention can be effectively reduced RSSI signal fluctuation, the signal waveform of output smoothing.
Description
Technical field
The present invention relates to RSSI positioning and vehicle mobile positioning technique field, more particularly to one kind are mobile for tracking low speed
The RSSI approximating method of vehicle.
Background technique
With the Fast Construction of smart city, it will arrange a large amount of wireless sensor network, base in urban road two sides
Important real-time sampling of traffic information, state monitoring method are had become in the vehicle positioning technology of wireless signal.The location technology
It is that setting for WIFI network (can be connected with driver or the portable smart phone of passenger, tablet computer and notebook
It is standby) it is used as mobile terminal MT (Mobile terminal), the spy issued by mobile terminal (MT) is monitored by WIFI sniff equipment
It surveys claim frame (Probe Request), extracts MAC Address therein, timestamp (Time), signal strength (RSSI).In conjunction with
The latitude and longitude information of WIFI sniff equipment itself, can extrapolate the position data of low speed move vehicle.
At this stage, based on signal strength (RSSI) location technology have it is at low cost, low in energy consumption, suitable for blocking environment more
The features such as, positioning field application indoors is wider.RSSI signal propagation model and Study of filtering algorithm based on fixed terminal have
Very much, this also for realize low speed move vehicle tracking provide theoretical basis.But in practical applications, the mobile of vehicle can make
At the inevitable multipath fading of RSSI signal, noise jamming and obstacle are blocked during plus actual measurement is not considered
It influences, the validity of RSSI data is low, there are problems that very big signal fluctuation, seriously affects and pushes away to the positioning of move vehicle
It calculates.
Obviously, the RSSI approximating method based on fixed terminal is not particularly suited for move vehicle scene, and urgent need proposes a kind of use
In the RSSI approximating method of tracking low speed move vehicle.
Summary of the invention
In order to overcome the technical issues of proposing in above-mentioned background, the present invention is intended to provide a kind of for tracking low speed locomotive
RSSI approximating method, prior art RSSI can be overcome to obtain the problems such as error is big, signal fluctuation is strong.
Technical solution provided by the present invention be it is a kind of for tracking the RSSI approximating method of low speed move vehicle, comprising with
Lower step:
Step S1: arranging WIFI sniff equipment in conplane both sides of the road, determine its longitude and latitude position, and establish with
The relative coordinate system of track positioning system；
Step S2: Gaussian filtering processing is carried out to the RSSI signal that WIFI sniff equipment receives, determines decay mode
Signal damped expoential λ value in type obtains the radio signal attenuation model for meeting real road environment；
Step S3: the confinement features to be moved linearly based on vehicle under road environment, using worst error distance criterion pair
RSSI value carries out judgement screening, sets threshold probability value F；
Step S4: by the data after judgement screening, carrying out Kalman filter processing, effective RSSI letter after obtaining noise reduction
Number.
Step S5: with the real road environment radio signal attenuation model being derived by, it is corresponding to calculate RSSI value
Distance, so that it is determined that the relative distance of low speed move vehicle distance WIFI sniff equipment, reaches tracking effect.
The present invention proposes a kind of for tracking the RSSI approximating method of low speed move vehicle, and mobile environment can be overcome to influence institute
The big problem of bring RSSI value fluctuation range.Collected RSSI signal carries out Gaussian filtering when fixed to terminal first
Then processing is screened with the judgement of worst error distance criterion, then carries out Kalman filter to the RSSI signal after screening
Processing, effective RSSI signal after being denoised.It in whole process, is filtered with Gaussian, acquisition meets practical road
The radio signal attenuation model of road environment, and consider the changing rule of RSSI value in low speed move vehicle scene, introduce maximum miss
Poor distance criterion is screened, and after Kalman filter is handled, RSSI error can be effectively reduced, improve tracking accuracy.
Detailed description of the invention
Fig. 1 is that the present invention is a kind of for tracking the RSSI approximating method schematic illustration of low speed move vehicle；
Fig. 2 is the RSSI signal distribution plots in the embodiment of the present invention under road environment.
Specific embodiment
RSSI approximating method in the present invention is applied in tracking low speed move vehicle environment, and the design of most critical is:
It is filtered using Gaussian, obtains the radio signal attenuation model for meeting real road environment, and consider low speed locomotive
The changing rule of RSSI value in scene introduces worst error distance criterion and carries out data screening, finally carries out Kalman filter
Processing, overcomes the influence of noise, the waveform of output smoothing.
The embodiment of the invention provides a kind of for tracking the RSSI approximating method of low speed move vehicle, can filter out vehicle
Effective RSSI signal reaches the mesh for improving RSSI fitting effect to reduce the error amount of measurement experiment in mobile context
's.
In order to achieve the above technical purposes, below with reference to attached drawing, detailed retouch is carried out to the preferred embodiment of the present invention
It states；It should be appreciated that preferred embodiments are merely illustrative of the invention, rather than limiting the scope of protection of the present invention.
As shown in Figure 1, a kind of for tracking the RSSI approximating method of low speed move vehicle, comprising the following steps:
Step S1: arranging WIFI sniff equipment in conplane both sides of the road, determine its longitude and latitude position, and establish with
The relative coordinate system of track positioning system.Step includes:
(1) in the both sides of the road region of a certain plane, WIFI sniff equipment one, the mobile terminal I of known location are arranged
Mobile terminal label is made i, i=1~I by platform.
If the coordinate of WIFI sniff equipment is (xr,yr), the coordinate of mobile terminal is (xr+1,yr+1),…,(xr+i,
yr+i),…,(xr+I,yr+I)；When arranging terminal, the position of distance at equal intervals should be successively chosen respectively, i.e. end coordinates should meet
Condition:
In formula, d1,…,di,…,dIRespectively i-th terminal is the distance between to WIFI sniff equipment.
(2) by computer end MYSQL database connect WIFI sniff equipment, obtain from mobile terminal sniff to RSSI number
It is believed that breath, is divided into I channel storage.
If every mobile terminal is sampled J times, sampling obtains a RSSI value every time, then samples from i-th mobile terminal
The data of acquisition are represented by Ri,J=(rssii,1,…,rssii,j,…,rssii,J), wherein j=1~J, I platform mobile terminal exist
The RSSI data obtained in testing time can indicate R={ R1,J,…,Ri,J,…,RI,J}；Wherein, rssii,jIt is mobile whole for i-th
The RSSI value for holding jth time sampling to obtain, Ri,JThe RSSI set of J acquisition is sampled within the testing time for i-th mobile terminal.
Step S2: Gaussian filtering processing is carried out to the RSSI signal that WIFI sniff equipment receives, determines decay mode
Signal damped expoential λ value in type obtains the signal attenuation model for meeting real road environment.Step includes:
(1) in ideal free space, radio propagation loss generallys use logarithm-normal distribution model, and model is such as
Under:
In formula, ad1、ad2Respectively mobile terminal twice test the moment between WIFI sniff equipment at a distance from (m),
RSSIad1、RSSIad2Respectively mobile terminal is in ad1、ad2The RSSI (unit dB) that place measures, λ signal decay factor is not (
Different value is taken in same test environment).
For set Ri,J=(rssii,1,…,rssii,j,…,rssii,J), since J RSSI value is the change of Random Discrete
Amount, it is known that RSSI value is shown below about the density fonction of x.
(2) by being configured to the threshold value that Gaussian is filtered, reservation meets the RSSI signal of predetermined probabilities threshold value ρ,
Give up the RSSI signal less than probability threshold value ρ simultaneously, probability threshold value ρ is traditionally arranged to be 0.6, and expression is as follows:
In formula, σ is variance, and μ is mean value.RSSI value in selection range [the 0.15 σ+μ≤σ of x≤3.09+μ], if shared N
A, the new RSSI value aggregated label of i-th mobile terminal makees Ri,N=(rssii,1,…,rssii,n,…,rssii,N), rssii,n
For the RSSI value of i-th mobile terminal, n-th of time series after Gaussian filtering.To set Ri,NArithmetic average is carried out, is obtained
The average value of one timing RSSI value of distance；
(3) relationship of RSSI value Yu distance d is found out, so that it is determined that meeting the radio signal attenuation mould of real road environment
Type.
RSSI=- (10 λ log10D+A) (formula 7)
RSSI value average value in radio signal attenuation model, after parameter A takes Gaussian to filter, when d=1m；
Step S3: based on the mobile confinement features of vehicle " straight line " under road environment, using worst error distance criterion pair
RSSI value carries out judgement screening, sets threshold probability value F.Step includes:
(1) raw data acquisition: RSSI signal value M are taken before current time in certain time interval T, is by time stab
Set Tm,Tm=(t1,…,tm,…,tM), corresponding RSSI value is denoted as set Rssim,Rssim=(rssi1,…,
rssim,…,rssiM)；
(2) variation tendency judges: setting set x (m)={ (t1,rssi1),…,(tm,rssim),…,(tM,rssiM), it is right
Set x (m) makees least square fitting.Wherein x (m) is timestamp and corresponding RSSI value, tmFor m-th of timestamp, rssimFor
The corresponding RSSI value of m-th of timestamp.
If fitting a straight line L equation is Ax+By+C=0, according to straight slope K=-a/b, it can be determined that learn RSSI signal
The variation tendency of value.As K > 0, indicate that RSSI signal value is in rising trend；As K=0, indicate that RSSI signal value steadily becomes
Change；As K < 0, indicate that RSSI signal value is on a declining curve；
(3) threshold probability value F is determined: each point is denoted as set l, l=to the Euclidean distance of straight line L in set of computations x (m)
{l1,…,lm,…,lM, wherein lmFor the Euclidean distance of m-th of timestamp point to fitting a straight line L.It is flat that arithmetic is carried out to set l
And using result as threshold probability value F；
It further, whether is abnormal RSSI value by threshold decision current time RSSI value of threshold probability value F.To set
TmUsing following RSSI value filters, filtered set X (t) is obtained.
In formula, t is time series parameters；X (m) is current time measurement data, and X (t-1) is last moment measurement data,
X (t) is filtered data.
In a preferred embodiment of the invention, if m=30, obtained fitting a straight line L are as follows: 0.9801x+y+50.1076=0.
As a result as shown in Figure 2.
Step S4: by the data after judgement screening, carrying out Kalman filter processing, effective RSSI letter after obtaining noise reduction
Number.
The status predication equation of Kalman filter system:
X (t | t-1)=AX (t-1 | t-1)+BU (t)
P (t | t-1)=AP (t-1 | t-1) AT+Q
The state renewal equation of Kalman filter system:
X (t | t)=X (t | t-1)+Kg (t) (Z (t)-HX (t | t-1))
Kg (t)=P (t | t-1) HT/(HP(t|t-1)HT+R)
P (t | t)=(I-Kg (t) H) P (t | t-1)
In formula, X (t | t-1) is the RSSI value at the current time predicted according to last moment；X (t-1 | t-1) it is upper
One moment RSSI value obtains predicted value；A, B is measuring system system parameter matrix；U (t) is the control of current time measuring system system
Amount processed；P (t | t-1) is the corresponding covariance matrix of X (t | t-1)；P (t-1 | t-1) is the corresponding covariance square of X (t-1 | t-1)
Battle array；Q is system noise；Z (t) is the measured value of current time RSSI value；H is the parameter matrix of measuring system；Kg (t) is
Kalman filter gain；R is measurement noise；The updated value of P (t | t) current state；I is unit matrix.
Kalman filter by the actual measured value of system and can be estimated by the recurrence thought of " prediction-more new model "
Value carrys out Removing Random No, is derived with the measured value of the RSSI discreet value of last moment move vehicle and current time RSSI
The RSSI value of current state, so that the RSSI value of output is more smooth, the RSSI value of mapping output this move vehicle is passed through
Kalman filter treated effect picture.
Step S5: finally, the RSSI value obtained after Kalman filter is handled, substitutes into the real road environment being derived by
Radio signal attenuation model in, the corresponding distance of RSSI is calculated, to extrapolate the opposite of low speed move vehicle distance AP
Distance.
The present invention is primarily directed to based on the RSSI signal value wave encountered in wireless location technology tracking low speed move vehicle
Dynamic range is excessive, the rough problem of data, devises one kind and meets road environment vehicle " straight line " mobile confinement features
RSSI approximating method.This method corrects radio signal attenuation model first, is re-introduced into worst error distance criterion and carries out data sieve
Choosing finally carries out Kalman filter processing, overcomes the influence of noise, the waveform of output smoothing, and then realizes that tracking low speed is mobile
The purpose of vehicle.By instance analysis, the RSSI value of move vehicle has efficiently controlled wave after approximating method processing
Dynamic range.
Claims (5)
1. a kind of for tracking the RSSI approximating method of low speed move vehicle, which comprises the steps of:
Step S1, WIFI sniff equipment is arranged in conplane both sides of the road, determine its longitude and latitude position, and it is fixed to establish tracking
The relative coordinate system of position system；
Step S2, Gaussian filtering processing is carried out to the RSSI signal that WIFI sniff equipment receives, determined in attenuation model
Signal damped expoential λ value obtains the radio signal attenuation model for meeting real road environment.
Step S3, the confinement features to be moved linearly based on vehicle under road environment, using worst error distance criterion to RSSI value
Judgement screening is carried out, threshold probability value F is set；
Step S4, by the data after judgement screening, Kalman filter processing is carried out, effective RSSI signal after obtaining noise reduction.
2. according to claim 1 a kind of for tracking the RSSI approximating method of low speed move vehicle, which is characterized in that step
Suddenly S1 includes:
(1) in the both sides of the road region of a plane, WIFI sniff equipment one, the mobile terminal I platform of known location is arranged, will be moved
Dynamic terminal label makees i, i=1~I；
In relative coordinate system, if the coordinate of WIFI sniff equipment is (xr,yr), the coordinate of mobile terminal is (xr+1,yr+1),…,
(xr+i,yr+i),…,(xr+I,yr+I)；
(2) by computer end MYSQL database connect WIFI sniff equipment, obtain from mobile terminal sniff to RSSI data letter
Breath is divided into I channel storage；
If every mobile terminal is sampled J times, sampling obtains a RSSI value every time, then obtains from i-th mobile terminal sampling
Data be represented by Ri,J=(rssii,1,…,rssii,j,…,rssii,J), wherein j=1~J, I platform mobile terminal are being tested
The RSSI data obtained in time can indicate R={ R1,J,…,Ri,J,…,RI,J}；Wherein, rssii,jFor i-th mobile terminal
The RSSI value that j sampling obtains, Ri,JThe RSSI set of J acquisition is sampled within the testing time for i-th mobile terminal.
3. according to claim 1 a kind of for tracking the RSSI approximating method of low speed move vehicle, which is characterized in that step
Suddenly S2 includes:
(1) by being configured to the threshold value that Gaussian is filtered, retain the RSSI signal for meeting predetermined probabilities threshold value ρ, simultaneously
Give up the RSSI signal less than probability threshold value ρ, probability threshold value ρ is set as 0.6, and expression is as follows:
In formula, σ is variance, and μ is mean value；Take the RSSI value in range [the 0.15 σ+μ≤σ of x≤3.09+μ], if share it is N number of, i-th
The new RSSI value aggregated label of platform mobile terminal makees Ri,N=(rssii,1,…,rssii,n,…,rssii,N), rssii,nFor
The RSSI value of i-th mobile terminal, n-th of time series, wherein n=1~N, to set R after Gaussian filteringi,NIt is calculated
Art is average, obtains the average value of one timing RSSI value of distance；
(2) relationship of RSSI value Yu distance d is found out, so that it is determined that meet the radio signal attenuation model of real road environment,
RSSI=- (10 λ log10d+A)
RSSI value average value in radio signal attenuation model, after parameter A takes Gaussian to filter, when d=1m.
4. according to claim 1 a kind of for tracking the RSSI approximating method of low speed move vehicle, which is characterized in that step
Suddenly S3 includes:
(1) it raw data acquisition: takes before current time RSSI signal value M in certain time interval T, is set by time stab
Tm,Tm=(t1,…,tm,…,tM), wherein m=1~M, is denoted as set Rssi for corresponding RSSI valuem,Rssim=
(rssi1,…,rssim,…,rssiM)；
(2) variation tendency judges: setting set x (m)={ (t1,rssi1),…,(tm,rssim),…,(tM,rssiM), to set
X (m) makees least square fitting, and wherein x (m) is timestamp and corresponding RSSI value, tmFor m-th of timestamp, rssimIt is m-th
The corresponding RSSI value of timestamp；
If fitting a straight line L equation is Ax+By+C=0, according to straight slope K=-a/b, the variation of RSSI signal value is learnt in judgement
Trend；As K > 0, indicate that RSSI signal value is in rising trend；As K=0, RSSI signal value smooth change is indicated；When K < 0
When, indicate that RSSI signal value is on a declining curve；
(3) threshold probability value F is determined: each point is denoted as set l, l=to the Euclidean distance of straight line L in set of computations x (m)
{l1,…,lm,…,lM, wherein lmFor the Euclidean distance of m-th of timestamp point to fitting a straight line L；It is flat that arithmetic is carried out to set l
And using result as threshold probability value F；
It whether is abnormal RSSI value by threshold decision current time RSSI value of threshold probability value F；To set TmUsing following RSSI
Value filter obtains filtered set X (t).
In formula, t is time series parameters；X (m) is current time measurement data, and X (t-1) is last moment measurement data, X (t)
For filtered data.
5. according to claim 1 a kind of for tracking the RSSI approximating method of low speed move vehicle, which is characterized in that step
Suddenly S4 includes:
The status predication equation of Kalman filter system:
X (t | t-1)=AX (t-1 | t-1)+BU (t)
P (t | t-1)=AP (t-1 | t-1) AT+Q
The state renewal equation of Kalman filter system:
X (t | t)=X (t | t-1)+Kg (t) (Z (t)-HX (t | t-1))
Kg (t)=P (t | t-1) HT/(HP(t|t-1)HT+R)
P (t | t)=(I-Kg (t) H) P (t | t-1)
In formula, X (t | t-1) is the RSSI value at the current time predicted according to last moment；X (t-1 | t-1) it is upper a period of time
It carves RSSI value and obtains predicted value；A, B is measuring system parameter matrix；U (t) is the control amount of current time measuring system；P(t|t-
It 1) is the corresponding covariance matrix of X (t | t-1)；P (t-1 | t-1) is the corresponding covariance matrix of X (t-1 | t-1)；Q is system noise
Sound；Z (t) is the measured value of current time RSSI value；H is the parameter matrix of measuring system；Kg (t) is Kalman filter gain；R
To measure noise；The updated value of P (t | t) current state；I is unit matrix.
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