1 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

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

3 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

4 System Design Blind node broadcasts request to the reference nodes Reference nodes reply by sending their coordinates and RSSI values

5 Original CC2431 Location Estimation Algo.

6 Proposed Location Estimation Algo.

7 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 log 10 d + A) (1) n ： signal propagation constant d ： distance from sender A ： received signal strength at 1 meter distance

8 Relation Curve A=40, n=3

9 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)

10 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

11 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

12 Estimation Stages Prediction Stages Converted to distances RSSI = － (10n log 10 d + A) (1) Probabilistic Phase – Distance Estimation(cont.) est – estimationprev – measured Smoothed Range Range rate pred – predicted

13 Probabilistic Phase – Position Estimation A B C P

14 Probabilistic Phase – Position Estimation(cont.)

15 Experiment Setup

16 Experiment Results Time (sec)

17 Comparison of Distances Between Filtered RSSI and Unfiltered data

18 Comparison of Location Coordinates (X, Y) Computed by Iterative Trilateration Algo. & CC2431

19 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