Please wait a minute...
J4  2011, Vol. 45 Issue (9): 1521-1527    DOI: 10.3785/j.issn.1008-973X.2011.09.003
    
Wireless signal strength propagation model
base on cubic spline interpolation
CHEN Ling, XU Xiao-long, YANG Qing, CHEN Gen-cai
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

An accurate wireless signal strength propagation model with less parameters was proposed to increase the application efficiency of fingerprint based positioning approaches in wireless networks, and to address the problems of wireless signal attenuation models, e.g. low computation efficiency, build complexity, and difficulty of parameter setting. The proposed model is an empirical model, which changes the attenuation factor n of the classical attenuation model, in terms of a cubic interpolation spline function of distance or received signal strength indicator (RSSI). The proposed model does not introduce any new parameter and is easy to compute. Experimental results indicated that in an indoor corridor environment, by trained with 10% samples, the proposed model estimated the wireless signal strength with an average error less than 0.4 dBm. Employing the calibration data generated by the proposed model, the fingerprint based positioning approach achieved a mean error less than 1.3 m.



Published: 01 September 2011
CLC:  TP 311  
Cite this article:

CHEN Ling, XU Xiao-long, YANG Qing, CHEN Gen-cai. Wireless signal strength propagation model
base on cubic spline interpolation. J4, 2011, 45(9): 1521-1527.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2011.09.003     OR     https://www.zjujournals.com/eng/Y2011/V45/I9/1521


基于三次样条插值的无线信号强度衰减模型

为提高指纹识别定位技术在无线网络中的应用效率,解决无线信号强度衰减模型计算效率低、构建复杂以及参数难确定等问题,提出使用较少参数实现精确的无线信号强度传播模型.该模型是一种经验模型,其由距离或信号强度的三次样条插值函数来调整经典衰减模型中的衰减因子,无需增加额外参数,计算简单.实验结果显示:在室内走廊环境中,该模型以10%的样本数据进行训练后,无线信号强度预测误差不超过0.4 dBm,由该模型生成校准数据,利用指纹识别法可获得平均误差1.3 m以内的定位精度.

[1] BAHL P, PADMANABHAN V N. RADAR: an inbuilding RFbased user location and tracking system[C]∥ Proceeding of IEEE INFOCOM. Israel: IEEE, 2000: 775-789.
[2] HAZAS M, HOPPER A. Broadband ultrasonic location systems for improved indoor positioning[J]. IEEE Transactions on Mobile Computing, 2006, 5(5): 536-547.
[3] CHENG Y, CHAWATHE Y, LAMARCA A. Accuracy characterization for metropolitanscale WiFi localization[C]∥ Proceeding of MobiSys. Seattle: ACM, 2005: 233-245.
[4] Ascension technology corporation [EB/OL]. [2009-12-01]. http:∥www.ascensiontech.com.
[5] GONCALO G, HELENA S. Indoor location system using ZigBee technology[C]∥ Proceeding of International Conference on Sensor Technologies and Applications. Washington: IEEE, 2009:152-157.
[6] CHEHRI A, FORTIER F, TARDIF P M. On the TOA estimation for UWB ranging in complex confined area[C]∥ Proceeding of International Symposium on Signals, Systems and Electronics.[S.l.]: IEEE, 2007: 533-536.
[7] PENG R, SICHITIU M L. Angle of arrival localization for wireless sensor networks[C]∥ Proceeding of IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. [S.l.]:IEEE, 2006: 374-382.
[8] TAKENQA C M, KYAMAKY. A robust positioning system based on fingerprint approach[C]∥ Proceeding of ACM International Workshop on Mobility Management and Wireless Access. Greece: ACM, 2007: 1-8.
[9] CHEN Y, YANG Q, Yin J, et al. Powerefficient accesspoint selection for indoor location estimation[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(7): 877-888.
[10] ISKANDER M F, YUN Z. Propagation prediction models for wireless communication systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2002, 50(3): 662-673.
[11] 施亚妮, 李丽娟. FDTD方法吸收边界条件的研究及应用[J]. 计算机仿真, 2008, 25(7): 113-148.
SHI Yani, LI Lijuan. Research on absorbing boundary condition in FDTD method and its application[J]. Computer Simulation, 2008, 25(7): 113-148.

[12] YUAN M, ARIJIT D, JUNG B H. Conditions for generation of stable and accurate hybrid TDFD MoM solutions[J].IEEE Transactions on Microwave Theory and Techniques, 2006, 54(1): 2552-2563.
[13] 黄永明, 吕英华, 徐立,等. 室内无线传播中一种混合建模方法的改进[J]. 北京邮电大学学报, 2004, 27(1): 13-17.
HUANG Yongming, LU Yinghua, XU Li, et al. An improved hybrid method for modeling indoor radio propagation[J]. Journal of Beijing University of Posts and Telecommunications, 2004, 27(1): 13-17.
[14] Friis transmission equation [EB/OL]. [2009-12-01].http:∥en.wikipedia.org/wiki/Friis_transmission_equation.
[15] LASSABE F, CANALDA P, CHATONNAY P, et al. A Friisbased calibrated model for WiFi terminals positioning[C]∥ Proceeding of IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks. Taormina: IEEE, 2005: 382-387.
[16] AKL R, TUMMALA D, LI X. Indoor propagation modeling at 24GHz for IEEE 80211 networks[C]∥ Proceeding of IASTED International MultiConference on Wireless and Optical Communications, Wireless Networks and Emerging Technologies. Banff: IASTED/ACTA, 2006: 14-19.
[17] MEDBO J, BERG J E. Simple and accurate path loss modeling at 5GHz in indoor environments with corridors[C]∥ Proceeding of IEEE Vehicular Technology Conference.[S.l.]: IEEE, 2000: 30-36.
[18] WANG Y, JIA X, LEE H K. An indoors wireless positioning system based on wireless local area network infrastructure[C]∥ Proceeding of International Symposium on Satellite Navigation Technology Including Mobile Positioning and Location Services. Melbourne: SatNav, 2003: 21-34.
[19] AAMODT B K. CC2431 location engine[S]. Texas: Application Note AN042, 2006.
[20] EDWARDS A A, DENNIS J A. Polyfit  a computer program for fitting a specified degree of polynomial to data points[R]. London:UK National Radiological Protection Board, NRPBM 11, 1973.
[21] KEYS R G. Cubic convolution interpolation for digital image processing[J]. IEEE

[1] KE Hai-feng, YING Jing. Real-time license character recognition technology based on R-ELM[J]. J4, 2014, 48(2): 0-0.
[2] JIN Cang-hong, WU Ming-hui, YING Jing. A context-aware index based text extraction framework[J]. J4, 2013, 47(9): 1537-1546.
[3] ZHU Fan-wei, WU Ming-hui, YING Jing. Faceted Web search approach for large scale unstructured data[J]. J4, 2013, 47(6): 990-999.
[4] FENG Pei-en, LIU Yu, QIU Qing-ying, LI Li-xin. Strategies of efficiency improvement for Eclat algorithm[J]. J4, 2013, 47(2): 223-230.
[5] LIU Ying, CHEN Ling, CHEN Gen-cai, ZHAO Jiang-qi, WANG Jing-chang. Approach for collection selection based on click-through data[J]. J4, 2013, 47(1): 23-28.
[6] YIN Ting, XIAO Min, CHEN Ling, ZHAO Jiang-qi, WANG Jing-chang. CQPM based OLAP query log mining and recommendation[J]. J4, 2012, 46(11): 2052-2060.
[7] XIAO Min, CHEN Iing, XIA Hai-yuan, CHEN Gen-cai. Data warehouse native feature based OLAP querying with keywords[J]. J4, 2012, 46(6): 974-979.
[8] ZHANG Li-ping, LI Song, HAO Xiao-hong, HAO Zhong-xiao. Jrv rough Vague region relation[J]. J4, 2012, 46(1): 105-111.
[9] WU Ming-hui, YING Jing. Business process modeling and formal verification[J]. J4, 2011, 45(2): 280-287.
[10] FU Chao-yang, GAO Ji, ZHOU You-ming. Service discovery based on integrating lexical multi-level hashing
with subsumption semantics
[J]. J4, 2010, 44(12): 2274-2283.
[11] YANG Qing, CHEN Ling, CHEN Gen-Cai. Estimating walking distance based on single accelerometer[J]. J4, 2010, 44(9): 1681-1686.
[12] XIONG Wei, WANG Xiao-Tun. Method for mapping software dependability requirements
based on quality function deployment
[J]. J4, 2010, 44(5): 881-886.
[13] ZHANG Yin, HE Gao, DIAO Li-Na, ZHANG San-Yuan. Abstract state machine design of Internetware model[J]. J4, 2010, 44(5): 923-929.
[14] JIANG Chao, YING Jing, TUN Meng-Hui, et al. Feature increment oriented approach for software product line analysis[J]. J4, 2009, 43(12): 2142-2148.
[15] CHEN Bin, TAO Min. Mining associated and item-item correlated frequent patterns[J]. J4, 2009, 43(12): 2171-2177.