A COMBINED APPROACH FOR NLOS MITIGATION IN CELLULAR POSITIONING WITH TOA MEASUREMENTS

1. A COMBINED APPROACH FOR NLOS MITIGATION IN CELLULAR POSITIONINGWITH TOA MEASUREMENTS Fernaz Alimoğlu M. Bora Zeytinci

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

4. 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. SCATTERING DIFFRACTION LINE-OF-SIGHT SHADOWING REFLECTION NLOS error

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

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

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

10. ALGORITHMS USED:KALMAN FILTER (III) BIASING KALMAN FILTER • 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. Recall • This can be done by • increasing the measurement • error covariance matrix

11. Biasing Kalman

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

14. Constrainted Weigthed Least Squares Method (I) • Turns non linear equations into linear forms • Based on Lagrange multipliers theory • Findsthat satisfies

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

16. 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. 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. Simulation Results (I) • Linear trajectory: MS follows a linear path

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

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

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

22. 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.

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