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ENHANCED RSSI-BASED HIGH ACCURACY REAL-TIME USER LOCATION TRACKING

SYSTEM FOR INDOOR AND OUTDOOR ENVIRONMENTS

Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

Authors： Erin-Ee-Lin Lau, Boon-Giin Lee, Seung-Chul Lee, and Wan-Young Chung

Publisher： INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS

Present： Yu-Tso Chen

Date： Feb, 10, 2009

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Outline

1. Introduction 2. System Design 3. Experiment Setup and Results 4. Conclusions and Future Works

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Introduction

Track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error

By incorporating a radiolocation device (CC2431, Chipcon, Norway) which uses Zigbee

The device possesses a location estimation capability via RSSI

Computes distances based on the transmitted and RSS between blind node and reference nodes

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System Design

Blind node broadcasts request to the reference nodes

Reference nodes reply by sending their coordinates and RSSI values

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Original CC2431 Location Estimation Algo.

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Proposed Location Estimation Algo.

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Deterministic Phase

Calibrating RSSI values for each reference node

The feature of non-isotropic path loss due to the various transmission medium and direction in different environments

RSSI = － (10n log10d + A) (1)• n ： signal propagation constant

• d ： distance from sender

• A ： received signal strength at 1 meter distance

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Relation Curve

A=40, n=3

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Deterministic Phase (cont.)

If only a single n (propagation constant) is used for all reference nodes, miscalculation of the distance occurs

Propagation constant is calculated by reversing the linear RSSI equation as shown in (1)

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Probabilistic Phase – Distance Estimation

Main challenge in RSSI-based location tracking is its high sensitivity to the environmental changes

The mobile target does not move and yet, signal strength varies over time

Smoothing algo. is proposed to minimize the dynamic fluctuation of radio signal received

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Probabilistic Phase – Distance Estimation(cont.)

There is a correlation between current positions with previous location

The basic assumption for this smoothing algorithm is that the constant velocity motion

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Estimation Stages

Prediction Stages

Converted to distances

• RSSI = － (10n log10d + A) (1)

Probabilistic Phase – Distance Estimation(cont.)

est – estimation prev – measured

Smoothed RangeRange

rate

pred – predicted

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Probabilistic Phase – Position Estimation

2 2 2

2 2 2

2 2 2

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

AP A p A p

BP B p B p

CP C p C p

d x x y y

d x x y y

d x x y y

AB

C

P

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Probabilistic Phase – Position Estimation(cont.)

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Experiment Setup

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Experiment Results

Time (sec)

Time (sec)

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Comparison of Distances Between Filtered RSSI and Unfiltered data

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Comparison of Location Coordinates ( X, Y) Comp�uted by Iterative Trilateration Algo. & CC2431

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Conclusions & Future Works

Smoothing algorithm is not proposed in other systems

Apply the smoothing algorithm on distances instead of RSSI

More complicated experiment will be designed to verify the effectiveness of the proposed algorithm