1. 1/23 A COMBINED APPROACH FOR NLOS MITIGATION IN CELLULAR POSITIONING WITH TOA MEASUREMENTS Fernaz Alimoğlu M. Bora Zeytinci

2. 2/23 OUTLINE Location estimation – Application areas – Different methods Proposed solution Algorithms used – Kalman Filter – LOS/NLOS identification method – Constrained Weighted Least Squares Simulation environment Simulation results Conclusions

3. 3/23 LOCATION ESTIMATION: APPLICATION AREAS Emergency services Mobile advertising Location sensitive billing Fraud protection Asset tracking Fleet management Intelligent transportation systems Mobile yellow pages

4. 4/23 LOCATION ESTIMATION: DIFFERENT METHODS Time of arrival (TOA) Angle of arrival (AOA) Time difference of arrival (TDOA) Enhanced observed time difference (EOTD) Cell global identification (CGI) and Timing advance (TA) Signal strength (SS) Global Positioning System (GPS)

5. 5/23 NLOS error REFLECTION SHADOWING SCATTERING LINE-OF-SIGHT DIFFRACTION

6. 6/23 Proposed Solution : Kalman & CWLS (I) Variance calculation LOS/NLOS Identification LOS decision NLOS decision Unbiased Kalman Biased Kalman CWLS Estimate Range measurments Coordintes of BS’s

7. 7/23 Proposed Solutions: Kalman & CWLS (II) Sliding window with length 20 is used for variance calculation. Variance corresponding to each range measurement is kept in data base until the end of operation. Weighting matrix of CWLS is composed of calculated variances and range measurements. Kalman Filter is used to smooth range measurements. Biased or unbiased mode decision is done according to these variances.

8. 8/23 ALGORITHMS USED: KALMAN FILTER(I) Previous data Target motion model Priori estimate Model used in our simulation Prediction

9. 9/23 ALGORITHMS USED: KALMAN FILTER(II) Measurement(s) Priori estimate Posteriori estimate Model used in our simulation Correction

10. 10/23 Recall ALGORITHMS USED:KALMAN FILTER (III) Kalman filter works best at additive white Gaussian noise with zero mean. Kalman Filter cannot follow an unexpectedly high erroneous data such as an NLOS error. When an NLOS situation is detected the dependence of the estimation on the measurements should be decreased. This is called BIASING. BIASING KALMAN FILTER This can be done by increasing the measurement error covariance matrix

11. 11/23 Biasing Kalman

12. 12/23 LOS/NLOS IDENTIFICATION METHOD Can be implemented when a LOS error standard deviation is available. Rough standard deviation: is compared with the (known) standard deviation of the measurement in LOS situation ( ) –If the situation is NLOS –γ is choosen to be 1.35 to prevent false alarm –Moving window is used for LOS / NLOS identification.

13. 13/23 Performance Analysis of LOS/NLOS identification Measurements are taken from 5 base stations, with 2 of them are NLOS at the same time.

14. 14/23 Constrainted Weigthed Least Squares Method (I) Turns non linear equations into linear forms Based on Lagrange multipliers theory Finds that satisfies

15. 15/23 Constrainted Weigthed Least Squares Method (II) Cost function Advantage of weighting each measurment inversely proportional to error.

16. 16/23 Simulation Environment (I) Movement of MS is limited within a cell Seven cells are hexagonally placed Flexible cell size Should be realistic Linear movement & random movement is considered.

17. 17/23 Simulation Environment (II ) Direction, velocity, number of BS s (LOS & NLOS) are predetermined Number of samples in NLOS situation is determined by the obstruction length and velocity. BS s in NLOS situation are randomly selected. Measurment noise is white Gaussian noise. NLOS error has a uniform distribution between 0-1000m.

18. 18/23 Simulation Results (I) Linear trajectory: MS follows a linear path

19. 19/23 Simulation Results (II) Linear trajectory: MS follows a linear path Linear trajectory: MS follows a linear path

20. 20/23 Simulation Results(III) Random movement: MS follows a path with several turns

21. 21/23 Simulation Results (IV) Random movement: MS follows a path with several turns Random movement: MS follows a path with several turns

22. 22/23 Conclusion Results are close to FCC requirements. Kalman and CWLS enhance accuracy of the estimate. NLOS period followed by a LOS period; –Transient error; –If BS changes direction in NLOS period, error increases –Increase Kalman gain to increase dependence on measurements Tests with real data should be realized.

23. 23/23 References [1] A. H. Sayed, A. Tarighat, and N. Khajehnouri, “Network based wireless location,” IEEE Signal Processing Magazine, pp. 24–40, July 2005. [2] C. D. Wann, Y. M. Chen, and M. S. Lee, “Mobile location tracking with nlos error mitigation,” vol. 2, Global Telecommunications Conference (GLOBECOM’02). IEEE, 17-21 November 2002, pp. 1688–1692. [3] G. Apaydin, “Comparison of location-estimation techniques of GSM phones with the simulations,” Master’s thesis, Bogazici University, 2003. [4] K. W. Cheung, H. C.So, W. K. Ma, and Y. T. Chan, “Least squares algorithms for time-of-arrival-based mobile location,” IEEE Transactions on Signal Processing, vol. 52, no. 4, April 2004. [5] J. F. Liao and B. S. Chen, “Adaptive mobile location estimator with NLOS mitigation using fuzzy interference scheme,” 2005, Ed. ISCOM 2005, 20-22 November. [6] E.Brookner, Tracking and Kalman Filtering Made Easy. Wiley- Interscience, April 1998. [7] B. L.Lee, K.Ahmet, and H.Tsuji, “Mobile location estimation with NLOS mitigation using kalman filtering,” vol. 3. New Orleand, LA: Proc. IEEE Wireless Communications and Networking (WCNC’03), March 2003, pp. 1969–1973. [8] G. Welch and G. Bishop, An Introduction to Kalman Filter. UNCChapel Hill, 5 April 2004. [9] D. P. Bertsekas, Nonlinear Programming. Athena Scientific, 1995, pp. 253–269. [10] [Online]. Available: http://mathworld.wolfram.com/polynomial.htm [11] T. Rapaport, Wireless Communications: Principles and Practice, 2nd ed., ser. Communications engineering and emerging technlogies. Prentice Hall, 2002.

24. 24/23 Measurement noise with covariance matrix Driving noise with covariance matrix ALGORITHMS USED:KALMAN FILTER(IV) Calculating the Kalman gain “K” Target motion model Measurement(s) Aim is to minimize posteriori estimate error covariance Priori error cov. Posteriori error cov. Kalman gain