Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT
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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, andor 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 targets. 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.
- Target Direction, Range and Position Finding