CN109462820B - RSSI fitting method for tracking low-speed moving vehicle - Google Patents

RSSI fitting method for tracking low-speed moving vehicle Download PDF

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CN109462820B
CN109462820B CN201811368105.7A CN201811368105A CN109462820B CN 109462820 B CN109462820 B CN 109462820B CN 201811368105 A CN201811368105 A CN 201811368105A CN 109462820 B CN109462820 B CN 109462820B
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林永杰
黄紫林
许伦辉
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South China University of Technology SCUT
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    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
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Abstract

The invention discloses an RSSI fitting method for tracking a low-speed moving vehicle, which comprises the following steps: arranging WIFI sniffing equipment on two sides of a road on the same plane, and establishing a relative coordinate system of a tracking and positioning system; performing Gaussian filtering processing on the RSSI signal received by the WIFI sniffing equipment, determining a signal attenuation index lambda value in an attenuation model, and obtaining a signal attenuation model conforming to the actual road environment; and judging and screening the RSSI value, and performing Kalman filtering processing on the judged and screened data to obtain an effective RSSI signal. The method utilizes Gaussian filtering to obtain a wireless signal attenuation model which accords with the actual road environment, considers the variation rule of the RSSI value under the scene of a low-speed moving vehicle, introduces the maximum error distance criterion for screening, and finally carries out Kalman filtering processing. The invention can effectively reduce the RSSI signal fluctuation and output smooth signal waveform.

Description

RSSI fitting method for tracking low-speed moving vehicle
Technical Field
The invention relates to the technical field of RSSI positioning and vehicle moving positioning, in particular to an RSSI fitting method for tracking a low-speed moving vehicle.
Background
With the rapid construction of smart cities, a large number of wireless sensor networks are arranged on two sides of urban roads, and a vehicle positioning technology based on wireless signals becomes an important traffic information real-time acquisition and state monitoring method. According to the positioning technology, a smart phone, a tablet personal computer and a notebook (a device capable of being connected with a WIFI network) which are carried by a driver or a passenger are used as a mobile terminal MT (Mobile terminal), a detection Request frame (Probe Request) sent by the mobile terminal MT is monitored through WIFI sniffing equipment, and an MAC address, a timestamp (Time) and a signal strength (RSSI) are extracted. And the position data of the low-speed moving vehicle can be calculated by combining the longitude and latitude information of the WIFI sniffing equipment.
In the present stage, the positioning technology based on the signal strength indicator (RSSI) has the characteristics of low cost, low power consumption, suitability for a multi-shielding environment and the like, and is widely applied to the field of indoor positioning. The RSSI signal propagation model and the filtering algorithm based on the fixed terminal are researched a lot, and a theoretical basis is provided for tracking a low-speed moving vehicle. However, in practical application, the movement of the vehicle can cause inevitable small-scale fading of the RSSI signal, and in addition, the influence of noise interference and obstacle shielding in the actual measurement process is not considered, so that the validity of the RSSI data is low, a great signal fluctuation problem exists, and the dead reckoning of the mobile vehicle is seriously influenced.
Obviously, the fixed terminal-based RSSI fitting method is not suitable for a mobile vehicle scene, and an RSSI fitting method for tracking a low-speed mobile vehicle is urgently needed.
Disclosure of Invention
In order to overcome the technical problems in the background, the invention aims to provide an RSSI fitting method for tracking a low-speed moving vehicle, which can overcome the problems of large RSSI acquisition error, strong signal fluctuation and the like in the prior art.
The technical scheme provided by the invention is an RSSI fitting method for tracking a low-speed moving vehicle, which comprises the following steps:
step S1: arranging WIFI sniffing equipment on two sides of a road on the same plane, determining the longitude and latitude positions of the WIFI sniffing equipment, and establishing a relative coordinate system of a tracking and positioning system;
step S2: performing Gaussian filtering processing on the RSSI signal received by the WIFI sniffing equipment, determining a signal attenuation index lambda value in an attenuation model, and obtaining a wireless signal attenuation model conforming to the actual road environment;
step S3: based on the constraint characteristic of linear movement of vehicles under the road environment, judging and screening the RSSI value by adopting a maximum error distance criterion, and setting a threshold probability value F;
step S4: and performing Kalman filtering processing on the data after the screening judgment to obtain an effective RSSI signal after noise reduction.
Step S5: the distance corresponding to the RSSI value is calculated by applying the actual road environment wireless signal attenuation model obtained through derivation, so that the relative distance between the low-speed moving vehicle and the WIFI sniffing equipment is determined, and the tracking effect is achieved.
The invention provides an RSSI fitting method for tracking a low-speed moving vehicle, which can solve the problem of large fluctuation range of RSSI values caused by the influence of a moving environment. Firstly, Gaussian filtering processing is carried out on RSSI signals collected when a terminal is fixed, then screening is carried out by using the maximum error distance criterion, Kalman filtering processing is carried out on the screened RSSI signals, and the denoised effective RSSI signals are obtained. In the whole process, a wireless signal attenuation model meeting the actual road environment is obtained by applying Gaussian filtering processing, the change rule of the RSSI value in a low-speed moving vehicle scene is considered, the maximum error distance criterion is introduced for screening, and after Kalman filtering processing, the RSSI error can be effectively reduced, and the tracking precision is improved.
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FIG. 1 is a schematic diagram of the RSSI fitting method for tracking a low-speed moving vehicle according to the present invention;
fig. 2 is a distribution diagram of RSSI signals under a road environment according to an embodiment of the present invention.
Detailed Description
The RSSI fitting method is applied to the environment of tracking low-speed moving vehicles, and the most key concept is as follows: and acquiring a wireless signal attenuation model conforming to the actual road environment by utilizing Gaussian filtering processing, introducing a maximum error distance criterion for data screening by considering the variation rule of the RSSI value in a low-speed moving vehicle scene, and finally performing Kalman filtering processing to overcome the influence of noise and output a smooth waveform.
The embodiment of the invention provides an RSSI (received signal strength indicator) fitting method for tracking a low-speed moving vehicle, which can screen out effective RSSI signals in a vehicle moving scene, thereby reducing the error value of a measurement experiment and achieving the aim of improving the RSSI fitting effect.
In order to achieve the above technical objects, preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
As shown in fig. 1, an RSSI fitting method for tracking a low-speed moving vehicle includes the following steps:
step S1: WIFI sniffing equipment is arranged on two sides of a road on the same plane, the longitude and latitude positions of the WIFI sniffing equipment are determined, and a relative coordinate system of a tracking and positioning system is established. The method comprises the following steps:
(1) in the two side areas of a road on a certain plane, a WIFI sniffing device with a known position and a mobile terminal I station are arranged, and the mobile terminal is marked as I, I is 1-I.
Let the coordinate of the WIFI sniffing device be (x)r,yr) Coordinates of the mobile terminal are (x)r+1,yr+1),…,(xr+i,yr+i),…,(xr+I,yr+I) (ii) a When the terminal is arranged, positions with equal spacing distances are sequentially and respectively selected, namely, the terminal coordinates meet the conditions:
Figure BDA0001869129200000031
in the formula (d)1,…,di,…,dIThe distances from the ith terminal to the WIFI sniffing equipment are respectively.
(2) WIFI sniffing equipment is connected through a MYSQL database at a computer end, RSSI data information sniffed from the mobile terminal is obtained, and the RSSI data information is divided into I channels to be stored.
Assuming that each mobile terminal is sampled J times, each sampling obtains one RSSI value, the data sampled from the ith mobile terminal can be represented as Ri,J＝(rssii,1,…,rssii,j,…,rssii,J) Where J is 1 to J, RSSI data obtained by the I-station mobile terminal during the test time may indicate that R is { R ═ R1,J,…,Ri,J,…,RI,J}; wherein rssii,jRSSI value R obtained for j sampling of ith mobile terminali,JAnd sampling the RSSI set obtained by J times in the test time for the ith mobile terminal.
Step S2: and performing Gaussian filtering processing on the RSSI signal received by the WIFI sniffing equipment, determining a signal attenuation index lambda value in the attenuation model, and obtaining a signal attenuation model which accords with the actual road environment. The method comprises the following steps:
(1) in ideal free space, the radio propagation loss usually adopts a log-normal distribution model, which is as follows:
Figure BDA0001869129200000032
in the formula, ad1、ad2Respectively is the distance (m) between the mobile terminal and the WIFI sniffing equipment at two test moments, and the RSSIad1、RSSIad2Respectively mobile terminal at ad1、ad2Measured RSSI (in dB), lambda signal attenuation factor (taken as different values in different test environments).
For the set Ri,J＝(rssii,1,…,rssii,j,…,rssii,J) Since J RSSI values are randomly discrete variables, the density distribution function of the RSSI values with respect to x is shown as the following formula.
Figure BDA0001869129200000033
(2) By setting the threshold value of Gaussian filtering, RSSI signals meeting a preset probability threshold value rho are reserved, RSSI signals smaller than the probability threshold value rho are discarded, the probability threshold value rho is generally set to be 0.6, and the specific expression is as follows:
Figure BDA0001869129200000034
where σ is the variance and μ is the mean. Selecting the range of [0.15 sigma + mu is not less than x is not more than 3.09 sigma + mu]The RSSI values in the mobile terminal are set to be N, and the new RSSI value set of the ith mobile terminal is marked as Ri,N＝(rssii,1,…,rssii,n,…,rssii,N)，rssii,nAnd filtering the RSSI value of the nth time sequence of the ith mobile terminal after Gaussian filtering. For set Ri,NCarrying out arithmetic mean to obtain the mean value of the RSSI values at a certain distance;
Figure BDA0001869129200000041
Figure BDA0001869129200000042
(3) and solving the relation between the RSSI value and the distance d so as to determine a wireless signal attenuation model which accords with the actual road environment.
RSSI＝-(10λlog10d + A) (formula 7)
In the wireless signal attenuation model, after Gaussian filtering is carried out on a parameter A, the mean value of RSSI values when d is 1 m;
step S3: based on the constraint characteristic of vehicle 'linear' movement under the road environment, the RSSI value is judged and screened by adopting the maximum error distance criterion, and the threshold probability value F is set. The method comprises the following steps:
(1) collecting original data: taking M RSSI signal values in a certain time interval T before the current time, and stamping the time as a set Tm,Tm＝(t1,…,tm,…,tM) The corresponding RSSI value is recorded as the set Rssim,Rssim＝(rssi1,…,rssim,…,rssiM)；
(2) And (3) judging the variation trend: let the set x (m) { (t)1,rssi1),…,(tm,rssim),…,(tM,rssiM) And performing least square fitting on the set x (m). Where x (m) is the timestamp and corresponding RSSI value, tmFor the mth timestamp, rssimAnd the RSSI value corresponding to the mth timestamp.
And (4) assuming that the fitted straight line L equation is Ax + By + C is 0, and judging the change trend of the RSSI signal value according to the slope K of the straight line which is-a/b. When K is greater than 0, the RSSI signal value is shown to be in an ascending trend; when K is 0, the RSSI signal value is stably changed; when K is less than 0, the RSSI signal value shows a descending trend;
(3) determining a threshold probability value F: calculating Euclidean distance from each point in the set x (m) to the straight line L, and recording the Euclidean distance as a set L, L ═ L1,…,lm,…,lMIn which lmThe euclidean distance of the mth timestamp point to the fitted straight line L. Pair collectionCarrying out arithmetic average on the sum l and taking the result as a threshold probability value F;
Figure BDA0001869129200000043
Figure BDA0001869129200000044
further, whether the RSSI value at the current time is an abnormal RSSI value is determined by using the threshold probability value F as a threshold. For set TmThe filtered set x (t) is obtained using the RSSI value filter described below.
Figure BDA0001869129200000051
In the formula, t is a time series parameter; x (m) is the measured data at the current moment, X (t-1) is the measured data at the last moment, and X (t) is the filtered data.
In a preferred embodiment of the present invention, assuming that m is 30, the obtained fitting straight line L is: 0.9801x + y +50.1076 is 0. The results are shown in FIG. 2.
Step S4: and performing Kalman filtering processing on the data after the screening judgment to obtain an effective RSSI signal after noise reduction.
The state prediction equation of the 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 update equation of the 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 the formula, X (t | t-1) is the RSSI value of the current moment predicted according to the previous moment; x (t-1| t-1) is a predicted value of the RSSI value at the last moment; A. b is a system parameter matrix of the measuring system; u (t) is the control quantity of the system measured at the current moment; p (t | t-1) is a covariance matrix corresponding to X (t | t-1); p (t-1| t-1) is a covariance matrix corresponding to X (t-1| t-1); q is system noise; z (t) is the measured value of the RSSI value at the current moment; h is a parameter matrix of the measurement system; kg (t) is Kalman filtering gain; r is measurement noise; an updated value of P (t | t) current state; and I is an identity matrix.
The Kalman filtering can eliminate random noise by actual measurement values and estimated values of a system through a recursive idea of 'prediction-update model', the RSSI estimated value of the moving vehicle at the previous moment and the measurement value of the RSSI at the current moment are used for deducing the RSSI value at the current state, so that the output RSSI value is smoother, and an effect graph of the RSSI value of the moving vehicle after Kalman filtering processing is plotted and output.
Step S5: and finally, substituting the RSSI value obtained after Kalman filtering into the deduced wireless signal attenuation model of the actual road environment, and calculating the distance corresponding to the RSSI so as to calculate the relative distance between the low-speed moving vehicle and the AP.
The invention mainly aims at the problems of overlarge fluctuation range and unsmooth data of RSSI signal values in the process of tracking low-speed moving vehicles based on a wireless positioning technology, and designs an RSSI fitting method which accords with the linear movement constraint characteristic of vehicles in a road environment. The method firstly corrects a wireless signal attenuation model, then introduces a maximum error distance criterion for data screening, and finally carries out Kalman filtering processing to overcome the influence of noise and output a smooth waveform, thereby realizing the aim of tracking a low-speed moving vehicle. Through example analysis, the RSSI value of the moving vehicle is processed by the fitting method, so that the fluctuation range is effectively controlled.

Claims (2)

1. An RSSI fitting method for tracking a low-speed moving vehicle is characterized by comprising the following steps:
step S1, arranging WIFI sniffing equipment on two sides of a road on the same plane, determining the longitude and latitude positions of the WIFI sniffing equipment, and establishing a relative coordinate system of a tracking and positioning system; (1) arranging a WIFI sniffing device with a known position and a mobile terminal I station in areas on two sides of a plane road, and marking the mobile terminal as I, wherein I is 1-I;
in a relative coordinate system, let the coordinate of the WIFI sniffing device be (x)r,yr) Coordinates of the mobile terminal are (x)r+1,yr+1),…,(xr+i,yr+i),…,(xr+I,yr+I)；
(2) The WIFI sniffing device is connected with the MYSQL database at the computer end, RSSI data information sniffed by the mobile terminal is obtained, and the RSSI data information is divided into I channels for storage;
assuming that each mobile terminal is sampled J times, each sampling obtains one RSSI value, the data sampled from the ith mobile terminal can be represented as Ri,J＝(rssii,1,…,rssii,j,…,rssii,J) Where J is 1 to J, RSSI data obtained by the I-station mobile terminal during the test time may indicate that R is { R ═ R1,J,…,Ri,J,…,RI,J}; wherein rssii,jRSSI value R obtained for j sampling of ith mobile terminali,JSampling the RSSI set obtained by the ith mobile terminal for J times within the testing time;
step S2, performing Gaussian filtering processing on the RSSI signal received by the WIFI sniffing equipment, determining a signal attenuation index lambda value in an attenuation model, and obtaining a wireless signal attenuation model conforming to the actual road environment; the method specifically comprises the following steps:
(1) by setting the threshold value of Gaussian filtering, RSSI signals meeting a preset probability threshold value rho are reserved, RSSI signals smaller than the probability threshold value rho are abandoned, the probability threshold value rho is set to be 0.6, and the specific expression is as follows:
Figure FDA0002827714940000011
wherein, σ is variance, and μ is mean; taking the range of [0.15 sigma + mu is less than or equal to x is less than or equal to 3.09 sigma + mu]The RSSI values in the mobile terminal are set to be N, and the new RSSI value set of the ith mobile terminal is marked as Ri,N＝(rssii,1,…,rssii,n,…,rssii,N)，rssii,nFor the ith mobile terminal after Gaussian filtering(ii) the RSSI value of the nth time series, where N is 1 to N, for the set Ri,NCarrying out arithmetic mean to obtain the mean value of the RSSI values at a certain distance;
Figure FDA0002827714940000012
Figure FDA0002827714940000013
(2) the relation between the RSSI value and the distance d is solved, thereby determining a wireless signal attenuation model which accords with the actual road environment,
RSSI＝-(10λlog10d + A), λ represents the signal attenuation index λ,
in the wireless signal attenuation model, after Gaussian filtering is carried out on a parameter A, the mean value of RSSI values when d is 1 m;
step S3, judging and screening the RSSI value by adopting a maximum error distance criterion based on the constraint characteristic of the straight line movement of the vehicle under the road environment, and setting a threshold probability value F; the method comprises the following steps:
(1) collecting original data: taking M RSSI signal values in a certain time interval T before the current time, and stamping the time as a set Tm,Tm＝(t1,…,tm,…,tM) Where M is 1 to M, the corresponding RSSI value is taken as the set RSSIm,Rssim＝(rssi1,…,rssim,…,rssiM)；
(2) And (3) judging the variation trend: let the set x (m) { (t)1,rssi1),…,(tm,rssim),…,(tM,rssiM) A least squares fit to the set x (m), where x (m) is the timestamp and corresponding RSSI value, tmFor the mth timestamp, rssimThe RSSI value corresponding to the mth timestamp;
setting the fitted straight line L equation as Ax + By + C as 0, and judging the variation trend of the RSSI signal value according to the slope K of the straight line; when K is greater than 0, the RSSI signal value is shown to be in an ascending trend; when K is 0, the RSSI signal value is stably changed; when K is less than 0, the RSSI signal value shows a descending trend;
(3) determining a threshold probability value F: calculating Euclidean distance from each point in the set x (m) to the straight line L, and recording the Euclidean distance as a set L, L ═ L1,…,lm,…,lMIn which lmThe Euclidean distance from the mth timestamp point to the fitting straight line L; carrying out arithmetic mean on the set l and taking the result as a threshold probability value F;
Figure FDA0002827714940000021
Figure FDA0002827714940000022
judging whether the RSSI value at the current moment is an abnormal RSSI value or not by taking the threshold probability value F as a threshold value; for set TmObtaining a filtered set X (t) using an RSSI value filter,
Figure FDA0002827714940000023
in the formula, t is a time series parameter; x (m) is the measured data at the current moment, X (t-1) is the measured data at the last moment, and X (t) is the filtered data;
and step S4, performing Kalman filtering processing on the data after the judgment and screening to obtain an effective RSSI signal after noise reduction.
2. The RSSI fitting method for tracking a low-speed moving vehicle according to claim 1, wherein the step S4 comprises:
the state prediction equation of the 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 update equation of the 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 the formula, X (t | t-1) is the RSSI value of the current moment predicted according to the previous moment; x (t-1| t-1) is a predicted value of the RSSI value at the last moment; A. b is a measurement system parameter matrix; u (t) is the control quantity of the measuring system at the current moment; p (t | t-1) is a covariance matrix corresponding to X (t | t-1); p (t-1| t-1) is a covariance matrix corresponding to X (t-1| t-1); q is system noise; z (t) is the measured value of the RSSI value at the current moment; h is a parameter matrix of the measurement system; kg (t) is Kalman filtering gain; r is measurement noise; an updated value of P (t | t) current state; and I is an identity matrix.
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