This paper investigates 3D positioning in an indoor line of sight (LOS) and nonline of sight (NLOS) combined environment. It is a known fact that time-of-arrival-(TOA-) based positioning outperforms other techniques in LOS environments; however, multipath in an indoor environment, especially NLOS multipath, significantly decreases the accuracy of TOA positioning. On the other hand, received-signal-strength-(RSS-) based positioning is not affected so much by NLOS multipath as long as the propagation attenuation can be correctly estimated and the multipath effects have been compensated for. Based on this fact, a hybrid weighted least square (HWLS) RSS/TOA method is proposed for target positioning in an indoor LOS/NLOS environment. The identification of LOS/NLOS path is implemented by using Nakagami distribution. An experiment is conducted in the iRadio lab, in the ICT building at the University of Calgary, in order to (i) demonstrate the availability of Nakagami distribution for the identification of LOS and NLOS path, (ii) estimate the pass loss exponent for RSS technique, and (iii) verify our proposed scheme. 1. Introduction In the last few years, the interest in indoor location has significantly increased due to the importance of the 3D indoor localization with a high level of accuracy. Wireless location estimation [1] is usually based on measuring one or more radio signal propagation attributes. Different attributes of a received radio signal such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) have been widely applied in location estimation of a target, such as a mobile station (MS) [2–6]. Unfortunately, all of the aforementioned techniques require a line of sight (LOS) link between MS and a number of base stations (BSs) of known positions. In a nonline of sight (NLOS) environment, their performances can degrade significantly. In most indoor environments, NLOS, one of the dominant factors significantly affecting positioning accuracy, is inevitable. A variety of existing tests can identify whether a measurement fits in an LOS or a NLOS profile. Historical range measurements have been used for NLOS identification purposes in [7–9]. By applying a range scaling algorithm, the true range can be estimated by constrained minimization [7]. In [8], NLOS identification is implemented via a running variance of range estimates. Experimental results conducted in [9] indicate that the standard deviation of the range measurements in an NLOS environment is much larger than its LOS counterpart, which can also be used for
References
[1]
D. Wang, Multicarrier transmission for wireless location—range estimation and positioning, Ph.D. dissertation, Electrical Engineering Dept., University of Calgary, Calgary, Canada, 2010.
[2]
J. Caffery and G. L. Stuber, “Subscriber location in CDNA cellular networks,” IEEE Transactions on Vehicular Technology, vol. 47, no. 2, pp. 406–416, 1998.
[3]
D. Wang and M. Fattouche, “OFDM transmission for time-based range estimation,” IEEE Signal Processing Letters, vol. 17, no. 6, article 69, pp. 571–574, 2010.
[4]
K. Yu, J. P. Montillet, A. Rabbachin, P. Cheong, and I. Oppermann, “UWB location and tracking for wireless embedded networks,” Signal Processing, vol. 86, no. 9, pp. 2153–2171, 2006.
[5]
D. Wang and M. Fattouche, “Multipath mitigation for LOS TBRE using NDB OFDM transmission and phase correlation,” Electronics Letters, vol. 46, no. 21, pp. 1467–1468, 2010.
[6]
D. Wang, M. Fattouche, and F. M. Ghannouchi, “Fundamental limit of OFDM range estimation in a separable multipath environment,” Circuits, Systems, and Signal Processing. In press.
[7]
S. Venkatraman, J. Caffery, and H. R. You, “Location using LOS range estimation in NLOS environments,” in Proceedings of the Vehicular Technology Conference (VTC '02), pp. 856–860, May 2002.
[8]
J. Borras, P. Hatrack, and N. B. Mandayam, “Decision theoretic framework for NLOS identification,” in Proceedings of the 48th IEEE Vehicular Technology Conference (VTC '98), pp. 1583–1587, May 1998.
[9]
M. P. Wylie and J. Holtzman, “Non-line of sight problem in mobile location estimation,” in Proceedings of the 5th IEEE International Conference on Universal Personal Communications (ICUPC '96), pp. 827–831, October 1996.
[10]
S. Venkatraman and J. Caffery, “Statistical approach to non-line of- sight BS identification,” in Proceedings of the International Symposium on Wireless Personal Multimedia Communications (WPMC '02), pp. 296–300, October 2002.
[11]
I. Güven？, C. C. Chong, F. Watanabe, and H. Inamura, “NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics,” EURASIP Journal on Advances in Signal Processing, vol. 2008, no. 36, pp. 1–14, 2008.
[12]
S. Venkatesh and R. M. Buehrer, “Non-line-of-sight identification in ultra-wideband systems based on received signal statistics,” IET Microwaves, Antennas and Propagation, vol. 1, no. 6, pp. 1120–1130, 2007.
[13]
G. L. Stuber, Principles of Mobile Communication, Kluwer Academic Publishers, 1996.
[14]
A. Abdi, K. Wills, H. A. Barger, M. S. Alouini, and M. Kaveh, “Comparison of the level crossing rate and average fade duration of Rayleigh, Rice, and Nakagami fading models with mobile channel data,” in Proceedings of the 52nd Vehicular Technology Conference (VTS '2000), pp. 1850–1857, September 2000.
[15]
M. Nakagami, “The m-distribution—a general formula for intensity distribution of rapid fading,” in Proceedings of the Symposium on Statistical Methods in Radio Wave Propagation, pp. 3–36, Pergamon Press, January 1960.
[16]
M. P. Wylie and J. Holtzman, “Non-line of sight problem in mobile location estimation,” in Proceedings of the 5th IEEE International Conference on Universal Personal Communications (ICUPC '96), pp. 827–831, October 1996.
[17]
P. C. Chen, “A non line of sight error mitigation algorithm in location estimation,” in Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 316–320, September 1999.
[18]
D. Wang, H. Leung, M. Fattouche, and F. M. Ghannouchi, “Efficient spectrum allocation and TOA-based localization in cognitive networks,” Wireless Personal Communications. In press.
[19]
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, pp. 1121–1128, 2004.
[20]
G. Zhao, D. Wang, M. Fattouche, and M. Jin, “Novel wireless positioning system for OFDM-based cellular networks,” IEEE Systems Journal. In press.
[21]
D. Wang, M. Fattouche, and F. M. Ghannouchi, “Geometry-based doppler analysis for GPS receivers,” Wireless Personal Communications, vol. 62, pp. 1–13, 2011.
[22]
G. Zhao, D. Wang, and M. Fattouche, “Time sum of arrival based BLUE for mobile target positioning,” Advanced Science Letters, vol. 4, no. 1, pp. 165–167, 2011.
