A novel WIFI-oriented RSSI signal processing method for ...

A novel WIFI-oriented RSSI signal processing method for

tracking low-speed pedestrians

UK · Liverpool2019.07.16

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INTRODUCTION

Intelligent Transportation Systems (ITS)

Accurately obtaining the location

information of pedestrians, capturing

pedestrian behaviors, and predicting their

trajectories

In China, non-motorized transportation

accounts for about 50% of urban traffic

The key difficulty lies in the accurate estimation of the pedestrian position？

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INTRODUCTION

Usually, users rarely initiate a

location request. Meanwhile,

positioning using the GPS is

unreliable in dense urban areas

with tall buildings and/or narrow

streets, known as ‘urban

canyons’.

Most computer vision systems are

designed to work under optimal

conditions such as clear skies,

low reflections, and few occlusions

etc.

There are multiple methods for tracking individuals, GPS (Global Positioning System) and Computer

vision video detection are the two most widely used outdoor positioning technology.

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INTRODUCTION

According to International Telecommunication

Union (ITU), as of 2013, the mobile phone world

average penetration rate is estimated 96.2%,

nearly the triple of that in 2005.

With the popularization of smartphones and

improvement of smartphones functions, the

integrated sensors become more and more

abundant, which makes it possible to provide

Location-Based Service (LBS) to users at anytime

and anywhere by smartphones.

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RSSI DATA FILTERING

According to the IEEE 802.11 series protocol,

the wireless mobile terminal with WiFi opened

will periodically inform the network of its

existence by sending a probe request frame

on the basis of the active scanning.

Therefore, capturing the probe request frames

broadcast by the wireless mobile terminal to

analyze the Media Access Control (MAC)

addresses, the number of the wireless mobile

terminals with WiFi opened in the monitoring

range can be known.

Core

Edge

Network

Hirarchy

User Layer

Fog Layer

Cloud Layer

Pedestrian Mobile Terminal Access Point Central Server Database Interchanger

WiFi

A. Sniffing and Data Collection

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RSSI DATA FILTERING

Actually, the RSSI value of such fluctuations

collected by the WiFi sniffer cannot be directly

used if there is not any processing and

optimization.

At the traffic environment, the coordinates of

low-speed moving targets (pedestrians) are

dynamic and unpredictable, which needs the

filtering algorithm to eliminate the

fluctuance of RSSI data from users mobile

phones while adapting to the changes of the

spectrum over

B. Moving Adaptive RSSI (CVKF) Filter Method

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RSSI DATA FILTERING

Nowadays, many researchers have used the

Kalman filter algorithm to remove the additive

noise superimposed on the signal, but cannot

remove the error. If the error of the last several

tested RSSI values is very large, it will have a

serious impact on the position result.

To deal with this problem, this study proposes a

constant-velocity Kalman filtering fusion

algorithm to reduce the RSSI value sequence.

Shows the framework of the algorithm. The

algorithm firstly smooths the collected RSSI

values based on the constant velocity filtering,

and then performs Kalman filtering based on the

smoothed RSSI values.


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RSSI DATA FILTERING


1) Constant velocity filter

Estimation:^ ^ ^

( ) ( ) ( )( )( )est i pred i pred iprev iR R a R R

^ ^ ^

( )( ) ( ) ( )( )pred iest i pred i prev i

S

bV V R R

T

Prediction:^ ^ ^

( +1) ( ) ( )pred i est i est i SR R V T

^ ^

( +1) ( )pred i est iV V

Where：^

( )est iR

^

( )pred iR

( )prev iR

^

( )est iV

^

( )pred iV ,a b

sT

=the smoothed estimate range at interval i;

=the predicted range at interval i;

=the measured range at interval i;

= the smoothed estimate range rate at interval i;

=the predicted range rate at interval i; =gain constant;

=the duration of updated time interval.

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RSSI DATA FILTERING


2) Kalman filter

a) Calculate the priori estimate of RSSI at the moment RSSI(t|t-1)

( | 1) ( 1| 1)RSSI t t A RSSI t t

b) The covariance of the priori estimate

( | 1) ( 1| 1) ( )P t t P t t Q t

c) Kalman filter gain of the system

( | 1)( )

( | 1) ( )

P t tKg t

P t t R t

d) The optimal estimation of the current RSSI value RSSI(t|t)

( | ) ( | 1) ( )( ( ) ( | 1))RSSI t t RSSI t t Kg t RSSI t RSSI t t

e) The covariance of the optimal estimation

( | ) ( ( )) ( | 1)P t t I Kg t P t t

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RSSI DATA FILTERING


3) Distance Estimation

10| | 10RSSI n log d B

1 2 3

1 2 3

3

3

x x xx

y y yy

After obtaining the distance value of the RSSI value, the weighted trilateration method is used to perform the

positioning, and the predicted position of the moving target can be obtained, as shown in the following formula

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EXPERIEMTAL RESULTS AND ANALYSIS

A. Experiment and Application Environment

In order to collect the query results of WiFi frames, we conducted experimental tests at South China University

of Technology, China. Four WiFi sniffers are deployed according to the actual situation as shown in Figure. We

use the DS-007 WiFi sniffer produced by Chengdu DataSky Company, which is suitable for outdoor

environments.

At the same time, the settings are sent to the server

every 1 second (the default value is adjustable).

As usual, the collected data includes RSSI value,

MAC address, and timestamp.

Experiment date: October 13, 2018

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B. Results and Discussions

1) Comparison of filtering performance between static and dynamic environments

At the static environment without accessibility, the RSSI value

has obvious fluctuation. When the filtering algorithm is adopted,the fluctuation of the RSSI signal is significantly reduced.

Meanwhile, the peak of error still has a hysteresis effect on the

Kalman curve.

In the real traffic environment, we can't eliminate the possibility

that the error peak appears at the end of data. As long as the

obvious error peak appears at the end of the data, it will have a

greater impact on the optimization result of the Kalman filter

algorithm.

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2) Estimation of the distance from the node to the moving target

The results showed that the estimated distance of the original

RSSI data is farther than the real distance. It is more accurate tocalculate the distance of the relative nodes by reducing the

fluctuation of the RSSI signal, which means that the improved

positioning accuracy is closely related to the filtered RSSI data.

In the static environment, the average error between the original

distance and the predicted distance is about 1.39m, the average

error after Kalman filter processing is 0.45m, and the average

error after processing by the CVKF filter is 0.33m. Assuming

that the distance is linear with the velocity, the average error

between the original distance and the predicted distance is

1.05m, the average error after Kalman filter processing is

0.44m, and the average error after CVKF filter processing is

0.32m.

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2) Estimation of the distance from the node to the moving target

The results showed that compared with the original RSSI

data, the average error of the X-axis direction of the

positioning accuracy after CVKF filter processing is

reduced from 0.67m to 0.16m, and the average error in the

Y-axis direction is reduced from the original data of 1.23m

to 0.15m.

The iterative trilateration measurement results of calculating

the distance according to the filtered signal strength showed

that the calculation method is effective in providing a more

accurate position, and the CVKF filter can greatly

improve the positioning accuracy when performing

positioning.

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CONCLUSIONS

The RSSI sample data collected by the WiFi device in a short time is small in the real-time tracking

process, the variance is very large when estimating the moving target position, and the position

movement is not smooth, which seriously affects the positioning performance and stability of the

system.

Therefore, this paper aims at addressing this problem, and proposed a new fusion RSSI signal filter

algorithm with integrating constant speed algorithm and Kalman Filter to further improve the

positioning accuracy.

Future research:

Combining signal strength filtering and moving target position filtering to further improve thepositioning accuracy and reliability of the positioning system;

The tested scenarios are not sufficient to reflect the benefits of proposed models under differentconditions, and it is important to collect more field data to perform statistical analysis.

THANK YOU！

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