Using Maximum Likelihood Estimation for RSSI-based Localization System

碩士 === 國立宜蘭大學 === 電子工程學系碩士班 === 98 === In Cellular Communication System, location related applications have been an important discussion and proposed widely; location related applications include VoIP, data transmission and some applications. Location-based Services (LBS) is a kind of location as a...

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Bibliographic Details
Main Authors: Keng-Yu Wu, 吳耿瑜
Other Authors: Han-Chieh Chao Ph.D.
Format: Others
Language:zh-TW
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/84828221277493867047
Description
Summary:碩士 === 國立宜蘭大學 === 電子工程學系碩士班 === 98 === In Cellular Communication System, location related applications have been an important discussion and proposed widely; location related applications include VoIP, data transmission and some applications. Location-based Services (LBS) is a kind of location as a base to provide mobile services. When the Global Positioning System and portable navigation devices become more and more popular. LBS can provide accurate mobile positioning in location related applications. The main applications of LBS include personnel and vehicle tracking, traffic navigation, nearby information searching and patient tracking system, etc., the GPS navigation is one of the commonly applications. When you want to get the location of Mobile Station (MS), GPS is the commonly scenario we used. Due to power consumptions, hardware requirements and most of scenarios are use outside, etc restriction. It cause cannot suitable for all devices. We propose an approach to get the location of MS without the GPS information. This approach based Maximum Likelihood Estimation (MLE) to check the reliable value of base station and calculate the location of MS. This approach is applied to RSSI-based as measurements for location estimation. Due to when RSSI-based localization processed has low accuracy, spend much time, a lot of numbers of calculation and cannot check the reliable value of Base Station (BS). We propose an approach to provide high accuracy, reduce the number of calculation and check reliable value of BS by Maximum Likelihood Estimation. Keywords: RSSI-based Localization, Maximum Likelihood Estimation, Location-based Services