改进的对数衰减动态非视距定位

doi:10.3969/j.issn.0255-8297.2014.04.006

Journal of Applied Sciences ›› 2014, Vol. 32 ›› Issue (4): 372-378.doi: 10.3969/j.issn.0255-8297.2014.04.006

• Communication Engineering • Previous Articles Next Articles

Improved Localization in Dynamic Non-line-of-Sight Environments Using a Modified Log Path-Loss Model

WANG Ri-ming1,2, FENG Jiu-chao1

1. School of Electronic and Information Engineering, South China University
of Technology, Gongzhou 510641, China
2. School of Information Engineering, Guangdong University of Technology, Gongzhou 510006, China

Received:2013-12-01 Revised:2014-04-03 Online:2014-07-31 Published:2014-04-03

Abstract

Abstract: To reduce estimation error caused by static path loss factor in a log path-loss model, a modified log path-loss model is proposed in this paper based on statistical analysis on the Zigbee localization experimental data. In this model, a negative exponent function is used to describe the distance relation of the path loss factor with target nodes and fixed nodes to improve performance of the traditional log path-loss model. A maximum likelihood (ML) estimator and the corresponding Cramer-Rao lower bounds is then proposed and derived. Results of Zigbee localization experiments in laboratory and bus station demonstrate good performance with accurate localization and flexibility for varying environments.

Key words: log path-loss model, received signal strength (RSS), maximum likelihood estimation (MLE), nonline-of-sight (NLOS), wireless localization, Cramer-Rao lower bound (CRLB)

CLC Number:

TN911.7

WANG Ri-ming1,2, FENG Jiu-chao1. Improved Localization in Dynamic Non-line-of-Sight Environments Using a Modified Log Path-Loss Model[J]. Journal of Applied Sciences, 2014, 32(4): 372-378.

References

[1] ROOS T, MYLLYMAKI P, TIRRI H. A statistical modeling approach to location estimation[J]. IEEE Transactions on Mobile Computing, 2002, 1(1): 59-69.

[2] PATWARI N, HERO III A O, PERKINS M, et al. Relative location estimation in wireless sensor networks[J]. IEEE Transactions on Signal Processing, 2003, 51(8): 2137-2148.

[3] LI X. RSS-based location estimation with unknown pathloss model [J]. IEEE Transactions on Wireless Communications, 2006, 5(12): 3626-3633.

[4] MARTIN R K, KING A S, PENNINGTON J, et al. Modeling and mitigating noise and nuisance parameters in received signal strength positioning [J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5451-5463.

[5] CHAN Y T, HO K C. A simple and efficient estimator for hyperbolic location [J]. IEEE Transactions on Signal Processing, 1994, 42(8): 1905-1915.

[6] Hing Cheung So L L. Linear least squares approach for accurate received signal strength based source localization [J]. IEEE Transactions on Signal Processing. 2011, 59(8): 4035-4040.

[7] BECK A, STOICA P, LI J. Exact and approximate solutions of source localization problems [J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1770-1778.

[8] CHITTE S D, DASGUPTA S. Distance estimation from received signal strength under log-normal shadowing: bias and variance [J]. IEEE Signal Processing Letters, 2009, 16(3): 216-218.

[9] OUYANG R W, WONG A, LEA C. Received signal strength-based wireless localization via semidefinite programming: noncooperative and cooperative schemes [J]. IEEE Transactions on Vehicular Technology, 2010, 59(3): 1307-1318.

[10] WANG G. A semidefinite relaxation method for energy-based source localization in sensor networks [J]. IEEE Transactions on Vehicular Technology, 2011, 60(5): 2293-2301.

[11] MONIR Vaghefi R M, GHOLAMI M R, BUEHRER R M, et al. Cooperative received signal strength-based sensor localization with unknown transmit powers[J]. IEEE Transactions on Signal Processing. 2013, 61(6): 1389-1403.

[12] AHN H S, YU W. Environmental-adaptive RSSI-based indoor localization [J]. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 626-633.

[13] COLUCCIA A. Reduced-bias ML-based estimators with low complexity for self-calibrating RSS ranging [J]. IEEE Transactions on Wireless Communications. 2013, 12(3): 1220-1230.

[14] KIM W, LEE J G, JEE G. The interior-point method for an optimal treatment of bias in trilateration location[J]. IEEE Transactions on Vehicular Technology, 2006, 55(4): 1291-1301.

[15] YU K, GUO Y J. Improved positioning algorithms for nonline-of-sight environments [J]. IEEE Transactions on Vehicular Technology, 2008, 57(4): 2342-2353.

[16] VALLET J E, KALTIOKALLIO O, SAARINEN J, et al. On the Sensitivity of rss based localization using the log-normal model: an empirical study [C].// 2013 10th Workshop on Positioning Navigation and Communication (WPNC ), 2013:1-6.

[17] GOLDSMITH A. Wireless communications[M]. [S.l.]: Cambridge University Press, 2005.

[18] Kay S M. Fundamentals of statistical signal processing, volume I: Estimation theory [M]. [S.l.]: Person Education, 1993.

[19] YU K, SHARP I, GUO Y J. Ground-based wireless positioning [M]. Wiley. com, 2009.

Improved Localization in Dynamic Non-line-of-Sight Environments Using a Modified Log Path-Loss Model

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 1

Recommended Articles

Metrics

Comments