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室内定位信号强度—距离关系模型构建与分析 被引量:4

The Construction and Analysis of the Indoor Positioning Received Signal Strength Indication-Distance Relationship Model
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摘要 室内定位的关键技术之一在于室内距离的精确确定。基于WiFi信号强度确定室内距离的技术主要是利用WiFi信号在传播路径中发生衰减的原理实现位置推算。根据室内WiFi信号强度随距离变化这一物理特性,基于对数-距离模型,通过对实测信号强度(RSSI)与距离进行拟合,构建了基于信号强度—距离的室内定位多项式模型,并对其进行精度评定,实现了模型的优化,提高了信号强度转化距离的精确度。结果表明,采用对数-距离模型和对数拟合模型计算距离与真实距离的平均偏差为0.73m和0.56m,新设计的信号强度-距离多项式模型解算结果平均偏差为0.26m,优于之前两种模型,可为相关研究提供参考。 The principle of indoor distance determination using WiFi signal strength indication is based on the reduction of WiFi signal strength in the course of transmission.According to this physical characteristic,based on the logarithmic-distance model,an indoor distance polynomial model has established based on the received signal strength indication(RSSI)and the actual distance fitting analysis.Furthermore,the precision assessment has been carried on,and the optimization of the mode has been realized,with an improvement of the accuracy of indoor distance determination.Results show that,the average deviations between the distances the logarithmic-distance model and logarithm fitting model calculated and the actual distance are 0.73 mand 0.56 m,respectively.The average deviation between the distance calculated by the polynomial model based on signal strength-distance and the actual distance is 0.26 m,which is much better than the results of the former two models.The results can provide reference for similar research.
出处 《现代测绘》 2018年第1期23-25,共3页 Modern Surveying and Mapping
基金 江苏省高校学校大学生创新创业训练计划项目 南京林业大学大学生创新训练计划项目(201510298114X) 江苏省测绘地理信息科研项目(JSCHKY201617)
关键词 室内定位算法 信号强度 信号强度-距离模型 indoor positioning algorithm signal strength signal strength-distance model
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  • 1刘成.LBS定位技术研究与发展现状[J].导航定位学报,2013,1(1):78-83. 被引量:53
  • 2LIU C E. Research on self-localization and target tracking in wireless sensor networks [ D ]. Beijing: Institute of A- coustics, Chinese Academy of Sciences, 2006.
  • 3AKYLDIZ I F, SU W L, SANKARASUBRAMANIAM Y, et al. A survey on sensor networks [ J ]. IEEE Communi- cations Magazine ,2002,40( 8 ) : 102-114.
  • 4BAI-IL P,PADMANABHAN V N. RADAR: An in-build- ing RF-based user localization and tracking system [ C ]. Proc of INFOCOM' 2000, TEL Aviv, Israel, 2000, 2: 775-784.
  • 5BLUMENTHAL J, GROSSMANN R,GOLATOWSKI F, et al. Weighted centroid localization in zigbee-based sensor net- works [ C ] Intelligent Signal Processing, 2007. WISP 22X/7. IEEE International Symposium on,2007:1-6.
  • 6PIVATO P, FONTANA L, PALOPOLI L, et al. Experi- mental assessment of a RSS-based localization algorithm in indoor environment[ C]. Instrumentation and Measure- ment Technology Conference (I2MTC) ,2010:416-421.
  • 7BUJA A, SWAYNE D F, LITTMAN M, et al. XGvis: Interactive data visualization with multidimensional scaling[ J ]. Journal of Computational and Graphical Statistics, 2004,5 : 21-53.
  • 8SHANG Y, RUML W, ZHANG Y. Localization from mere connectivity in sensor networks[ C]. Proc of the 4th ACM Int'l Symp on Mobile Ad Hoc networking & computing, New York: ACM Press,2003:201-212.
  • 9SHANG Y, RUML W. Improved MDS-based localization [ C ]. Proceedings of the 23rd Conference of the IEEE Communications Society, Hong Kong, China, 2004: 2640-2651.
  • 10秦爽.参数化多维标度定位方法研究[D].成都:电子科技大学.2013.

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