Algorithmic Insufficiency of RSSI Based UKF for RFID Localization Deployment On-Board the ISS

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Author(s)
Carnes, Joshua T.
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Organizational Unit
Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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Abstract
This work evaluates the application of Unscented Kalman Filter (UKF) to generate stochastic localizations of radio frequency identification (RFID) chips in a sensor poor, highly reflective environment. Localization is done through the application of kNN algorithms and UKF methods to assign to reference RFID tags. The research is conducted in response to the needs of NASA for an application on the International Space Station. While the UKF has been shown to be effective on RFID streams, the sensor poor environment and difficult conditions aboard the ISS cause a loss of localization. This work shows that a UKF alone is insufficient for deployment on the ISS and proposes an alternative. Validation methods are proposed, and initial results are generated. Current industry methods are explored as benchmarks for algorithm performance.
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Date
2018-05-01
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Resource Type
Text
Resource Subtype
Masters Project
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