Abstract

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.

Highlights

  • During the past two decades, there has been an exceptional development in localization and positioning field benefiting from global positioning system (GPS) [1]

  • To conduct the OFAT analysis experiment, a couple of tests with different settings of controllable factors were conducted in the simulation system

  • It should be noted that a notation was used to describe a level range and a {levels} notation was used to Attenuation factor Signal noise reference points (RPs) density

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Summary

Introduction

During the past two decades, there has been an exceptional development in localization and positioning field benefiting from global positioning system (GPS) [1]. There are already many commercial off-the-shelf (COTS) systems for indoor localization, such as Situm (https://situm.es/en), Kio-RTLS (http://www.eliko.ee), OpenRTLS (https://openrtls .com), Aero Scout (http://www.aeroscout.com), and Ubisense (http://ubisense.net/en). Among these different indoor localization systems, location fingerprinting is a solution using scene analysis method and usually working with wireless local area network (WLAN) received signal strength indicator (RSSI), for example, the Skyhook (http://www.skyhookwireless.com/) commercial positioning system. The scene analysis algorithm consists of two phases: offline learning and online positioning It builds a location fingerprint database on some reference points (RPs) with given location coordinates during the offline learning phase. In the online positioning phase, the main work is to search the nearest RP or RPs by using RSSI received on an unknown point. There are two kinds of common searching algorithms in use: the deterministic [6] and probabilistic [7, 8] algorithms

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