Accession Number : ADA622808


Title :   Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks


Descriptive Note : Master's thesis


Corporate Author : AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT


Personal Author(s) : Nishida, Jeffrey K


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a622808.pdf


Report Date : 26 Mar 2015


Pagination or Media Count : 109


Abstract : Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT).


Descriptors :   *TARGETS , CLUSTERING , DETECTION , DETECTORS , POSITION(LOCATION) , THESES , TOMOGRAPHY , TRACKING , WIRELESS LINKS


Subject Categories : Cybernetics
      Target Direction, Range and Position Finding


Distribution Statement : APPROVED FOR PUBLIC RELEASE