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Abstract:
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In a smart environment , accurate locations of objects are a fundamental and critical issue . To achieve this goal , we present several methods based on passive far -field UHF RFID technologies , which can satisfy accuracy , robustness and reliability , cost efficiency , simplicity , compatibility , and scalability . Our research overcomes several negative characteristics of the use of cost efficient passive UHF RFID . Our research has several important contributions .First , we study the causes of the problems of using passive UHF RFID in localization , with detailed empirical results , and then , present the impacts of the causes on existing localization techniques such as KNN . Second , we present a new model of backscattered signal strength for passive far -field UHF RFID system under tag -to -tag interference . We propose a method to estimate power variations due to tag interference , based on a tag -to -tag distance and angle using a second order under -damped system . We present a novel localization algorithm to estimate target object location using our Tag -to -tag Interference Model (LMTI ) . According to the empirical results , LMTI improves accuracy be over 200 % compared with RSSI based KNN algorithm when objects are empty boxes , and 127 % improvement when objects are the print cartridges contained in aluminum foil bags . Third , we present another approach to achieve accurate localization . Localization using Detection of Tag Interference (LDTI ) algorithm , which detects the tag interference on a map of reference tags to estimate target location . To avoid selection of spatially non -adjacent reference tags , we also present the most interfered reference group finding algorithm , which considers spatial relations between reference tags . LDTI based smart shelf performs on average 0 .0948m estimation error for 9 empty cardboard boxes , and average 0 .1831m estimation error for 9 print cartridge containers , which is a 71 % accuracy improvement compared to the KNN algorithm . Finally , we present a novel Vision and passive UHF RFID integrated Localization (VRL ) system on smart shelf application to improve performance under harsh conditions . VRL performs on average 0 .079m estimation error for 10 print cartridge containers , which is a 61 % accuracy improvement compared to the LDTI algorithm under low False Negative Reference (FNR ) interrogation conditions . Moreover , it shows 555 % computation overhead reduction compared a homogeneous vision system . In high FNR conditions , VRL system achieves over 620 % increased accuracy compared to LDTI , and 437 % reduced computation time compared to a pure vision based localization system . |