1. University of Texas at San Antonio A Method of Compensating the MAI Effects in Weak Signal Conditions for Satellite Positioning Systems Lacatus Catalin, David Akopian, Mehdi Shadaram Electrical and Computer Engineering Department The University of Texas at San Antonio

2. University of Texas at San Antonio Positioning Standards in Cellular Networks  FCC Enhanced 911 (E911) Mandate provides emergency services personnel with location information  Standardized location technologies: Cell-ID, E-OTD,OTDOA, A-GPS  Their applicability in cellular networks: GSM/GPRS and UMTS (3G) as shown below

3. University of Texas at San Antonio ephemeris, almanac, clock, ephemeris,... Assisted GPS (A-GPS) (wireless device + GPS + navigation) ephemeris, almanac, clock, #&”#¤&&((!”?!##%#&& ephemeris, almanac, clock... x,y,z, t DGPS, timing, xyz Ephemeris etc. broadcast lat = 61’29’’ long = 23’46’’ alt = 100m

4. University of Texas at San Antonio Problems : Indoors and Urban Canyon: inter-satellite signal interference

5. University of Texas at San Antonio cos(…) h 1 (t) d 1 (t) Dispreading Code Optimization Channel GPS SatellitesGPS receiver c 1 (t) d 1 (t) cos(…) SV1 c 2 (t) d 2 (t) cos(…) SV2 c n (t) d n (t) cos(…) SVn

6. University of Texas at San Antonio

7. Rationale  The Global Positioning System (GPS) is initially designed for outdoor sky-unobstructed usage.  The goal is to enable indoor and urban canyon operations when line-of-sight signals are weak.  The proposed approach improves the robustness of the GPS receivers by minimizing the inter-satellite signal interference

8. University of Texas at San Antonio Problem Statement and Solution  Reduce interferences between satellite signals using optimally-adapting de-spreading code  Perform adaptation in small increments to follow signal variations  Avoid computationally challenging operations such as matrix inversion

9. University of Texas at San Antonio GPS Receiver Structure RF Front-End ADC Baseband Positioning Augment. User Interfaces LBS Our Contribution is for robust Baseband (tracking) operation

10. University of Texas at San Antonio Example of an Optimal Dispreading Code

11. University of Texas at San Antonio Performance Improvement  BER performances for the beacon 1 Conventional Performance for 2 times weaker signal

12. University of Texas at San Antonio Performance Improvement  BER performances for the beacon 1 Conventional Performance for 50 times weaker signal

13. University of Texas at San Antonio Performance Improvement  BER performances for the beacon 1 Conventional Performance for 100 times weaker signal

14. University of Texas at San Antonio Market  Location Based Services (LBS): Analysts predict annual revenue to be anywhere from $18 billion to $33 billion  FCC Enhanced 911 (E911) in US  E112 in EU  Qualcomm acquired start-up SNAPTRACK in 2000-2001 for ~$1bln.  All major players are present (Nokia, Qualcomm, Motorola, Nortel, TI, STM, MITRE, Honeywell, Thales Navigation, Trimble)

15. University of Texas at San Antonio Claims  Conventional GPS transmitters and receivers are using the same codes. We variate de-spreading code for optimal inter- interference cancellation in weak signal conditions. This will improve robustness of receiver operation.  The algorithm adjusts codes adaptively to follow variations in received signals  Compared with other approaches our approach is not using computationally challenging operations such as matrix inverses  We deliver conventional performance for 2-100 times weaker signals. (Attenuation of signal indoors is of the order 10-300 times)

16. University of Texas at San Antonio Mathematical Model - unit-norm code sequence corresponding to beacon k ; - is the information symbol ; - is the received power from the beacon k ; - is the noise - AWGN ;

17. University of Texas at San Antonio Mathematical Model  Decision variable for the beacon k is  Correlation matrix of the received signal

18. University of Texas at San Antonio Problem Formulation  We define the MSE for each receiver channel k as  Optimization Problem

19. University of Texas at San Antonio Algorithm Derivation  The method has a global minimum and can be found by feasible directions method  Algorithm - iterations