Location estimation with data fusion for wireless location systems

Wireless location in wireless communications networks has been studied extensively in recent years. Among the network-based approaches, time of arrival (TOA) and time difference of arrival (TDOA) are two time-based techniques used in location estimation. It is known that the accuracy of location estimation may suffer from poor geometric dilution of precision (GDOP) situation and non-line of sight (NLOS) propagation effect. To provide more accurate measures for time-based location techniques under the effects of GDOP in wireless communications networks, a data fusion with fuzzy rules is presented. Based on the GDOP statistical properties of the raw location measures, the data fusion integrates data obtained from the TOA and TDOA techniques. Simulation results show that the proposed fuzzy logic-based data fusion effectively improves the overall accuracy of location estimation in wireless location systems.

[1]  James J. Caffery,et al.  Wireless Location in CDMA Cellular Radio Systems , 1999 .

[2]  J. Holtzman,et al.  The non-line of sight problem in mobile location estimation , 1996, Proceedings of ICUPC - 5th International Conference on Universal Personal Communications.

[3]  Julie A. Dickerson,et al.  Data fusion using the expected output membership function , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[4]  Julie A. Dickerson,et al.  Adaptive data fusion using the expected output membership function , 1999, Defense, Security, and Sensing.

[5]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[6]  S. Merigeault,et al.  Data fusion based on neural network for the mobile subscriber location , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[7]  Theodore S. Rappaport,et al.  Wireless position location: fundamentals, implementation strategies, and sources of error , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[8]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[9]  J. O'Connor,et al.  CDMA infrastructure-based location finding for E911 , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[10]  Gordon L. Stüber,et al.  Radio location in urban CDMA microcells , 1995, Proceedings of 6th International Symposium on Personal, Indoor and Mobile Radio Communications.

[11]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

[12]  Thomas Kleine-Ostmann,et al.  A data fusion architecture for enhanced position estimation in wireless networks , 2001, IEEE Communications Letters.

[13]  Maurizio Spirito,et al.  Further results on GSM mobile station location , 1999 .

[14]  Amitabh Mishra,et al.  A network architecture for global wireless position location services , 1999, 1999 IEEE International Conference on Communications (Cat. No. 99CH36311).

[15]  T. Rantalainen,et al.  Mobile station emergency locating in GSM , 1996, 1996 IEEE International Conference on Personal Wireless Communications Proceedings and Exhibition. Future Access.

[16]  P. Massatt,et al.  GEOMETRIC FORMULAS FOR DILUTION OF PRECISION CALCULATIONS , 1990 .

[17]  Maurizio Spirito,et al.  Mobile station location with heterogeneous data , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[18]  Jeffrey H. Reed,et al.  Position location using wireless communications on highways of the future , 1996, IEEE Commun. Mag..