1. 1 Broadband System Laboratory Advisor : Prof. Chin-Liang Wang Presented by Yih-Shyh Chiou August 8, 2005 後 卓 越 計 畫 進 度 報 告後 卓 越 計 畫 進 度 報 告

2. 2 A Radio Propagation Model (1/3) Predicted SNRs from the four APs as the MT walks along the upper hallway The floor layout of our experimental environment.

3. 3 A Radio Propagation Model (2/3) Ref. P. Tao, A. Rudys, A. M. Ladd, and D. S. Wallach, “Wireless LAN location-sensing for security applications,” in Proc. ACM Workshop on WISE, Sep. 2003. Observed signal strength histogram variation over time from a laptop to a fixed AP

4. 4 A Radio Propagation Model (3/3) Measured and predicted SNR from the AP1 in the four directions and their average result as the MT walks along the upper hallway from AP1 to AP2

5. 5 Why Extended Kalman Filter ? Kalman filter preferred –System model can predict the state for next epoch –Provide a simple automatic way of weighting all measurements in accordance with their statistical worth Kalman filter is formulated for linear systems Our measurement system is non-linear –Linearization is needed! Approximate the non-linear model with a set of linear model

6. 6 Simulation Results (1/5). Observed SNR trajectory from the four APs as the MT walks along the upper hallway from left to right

7. 7 Simulation Results (2/5) The SNR estimation by the proposed Kalman filter from the four APs as the MT walks along the upper hallway from left to right

8. 8 Simulation Results (3/5) Comparison between the SNR-based radio propagation modeling scheme (true trajectories ) and the MT estimated SNR trajectories scheme by the proposed SNR-based extended Kalman filter

9. 9 Simulation Results (4/5) Comparison between the KF-based estimated error and the EKF- based estimated error in terms of the SNR as the MT walks along the upper hallway from left to right

10. 10 Simulation Results (5/5) Comparison between the SNR-based (SS ratio) location schemes and the RPM-based location schemes in terms of the cumulative distribution function of the error distance