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首页> 外文期刊>Mobile Information Systems >An Analysis of Impact Factors for Positioning Performance in WLAN Fingerprinting Systems Using Ishikawa Diagrams and a Simulation Platform
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An Analysis of Impact Factors for Positioning Performance in WLAN Fingerprinting Systems Using Ishikawa Diagrams and a Simulation Platform

机译：基于Ishikawa图和仿真平台的WLAN指纹系统定位性能的影响因素分析

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摘要

Many factors influence the positioning performance in WLAN RSSI fingerprinting systems, and summary of these factors is an important but challenging job. Moreover, impact analysis on nonalgorithm factors is significant to system application and quality control but little research has been conducted. This paper analyzes and summarizes the potential impact factors by using an Ishikawa diagram considering radio signal transmitting, propagating, receiving, and processing. A simulation platform was developed to facilitate the analysis experiment, and the paper classifies the potential factors into controllable, uncontrollable, nuisance, and held-constant factors considering simulation feasibility. It takes five nonalgorithm controllable factors including APs density, APs distribution, radio signal propagating attenuation factor, radio signal propagating noise, and RPs density into consideration and adopted the OFAT analysis method in experiment. The positioning result was achieved by using the deterministic and probabilistic algorithms, and the error was presented by RMSE and CDF. The results indicate that the high APs density, signal propagating attenuation factor, and RPs density, with the low signal propagating noise level, are favorable to better performance, while APs distribution has no particular impact pattern on the positioning error. Overall, this paper has made great potential contribution to the quality control of WLAN fingerprinting solutions.
机译：许多因素影响WLAN RSSI指纹系统中的定位性能，这些因素的总结是一项重要但具有挑战性的工作。此外，对非算法因素的影响分析对于系统应用和质量控制具有重要意义，但很少进行研究。本文使用Ishikawa图分析和总结了潜在的影响因素，其中考虑了无线电信号的传输，传播，接收和处理。开发了一个仿真平台以方便分析实验，并考虑到仿真的可行性，将潜在因素分为可控制，不可控制，有害和保持恒定的因素。它考虑了AP的密度，AP的分布，无线信号的传播衰减因子，无线信号的传播噪声和RP的密度这五个非算法可控因素，并在实验中采用了OFAT分析方法。通过使用确定性和概率算法获得定位结果，并由RMSE和CDF表示误差。结果表明，高AP密度，信号传播衰减因子和RPs密度以及低信号传播噪声水平有利于更好的性能，而AP分布对定位误差没有特别的影响。总体而言，本文为WLAN指纹识别解决方案的质量控制做出了巨大的潜在贡献。

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