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A Particle Filter Based Framework For Indoor Wireless Localization Using Custom Ieee 802.15.4 Nodes

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A Particle Filter Based Framework For Indoor Wireless Localization Using Custom Ieee 802.15.4 Nodes

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Title: A Particle Filter Based Framework For Indoor Wireless Localization Using Custom Ieee 802.15.4 Nodes
Author: Dixit, Vijay Vasant
Abstract: Locating people and objects as soon as possible and fair amount of precision has always been an important part of any organization or industry, especially in manufacturing, healthcare, and logistics. Systems that now have the ability to locate objects or people are called Real Time Location Systems (RTLS). They typically use small low-power transmitters called tags attached to assets (or worn by people) as well as sets of readers that map the location of these tags. Systems that map the longitude and attitude of an object are geo-location systems and generally use GPS for location mapping. Systems that map a location relative to a fixed set of coordinates are more accurately called Real Time Location Systems. Several technologies are used to build up Real Time Location Systems. Some use dedicated tags and readers while others use existing WLAN networks and add RTLS ability to those networks. We propose a probabilistic approach to localization, based upon Received Signal Strength (RSSI) and mobility based data, like accelerometer and gyro readings. Global localization is a flavor of localization in which the device is unaware of its initial position and has to determine the same from scratch. The first step involves building wireless signal strength maps of the tag with respect to the Accesspoint. The map is built by measuring the RSSI readings of the mobile node relative to the Accesspoint at various intervals of distance and at each interval for different orientations. These readings form a sample set for Sequential Monte-Carlo sampling. Next, a posterior probability distribution for the location of the wireless device is computed over the entire area using Monte-Carlo sampling based Bayesian filtering, also known as Particle filters. Location estimates may then be determined from this distribution using the maximum density point or other parameters depending on the estimate needed. We discuss theory and research leading to the proposed method and provide results of real-life experiments.
URI: http://hdl.handle.net/10106/1818
Date: 2009-09-16
External Link: http://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=218

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