This paper presents our work on localizing a passive UHF RFID tagged object in an indoor workplace. We focus on uncontrolled settings with random orientations of the target object, dynamically moving people in the environment and cluttered rooms with many furniture items. Multiple fixed antennas are used to handle random tag orientations and human body effects. The antennas are placed in a way to minimize the obstruction for human activities and the effect of human presence and movement on the localization system. We use zone-based and exact localization methods incorporating probabilistic and deterministic machine learning techniques. We also propose a combined coarse-to-fine approach to improve accuracy and increase speed. Experimental results show that our system is able to localize an object with an error of 37 cm for exact localization and with an accuracy of 92% for zone-based classification. Experiments in challenging conditions showed that our overall design is robust to human body effects, even exploits the destructive effects of human body on UHF RFID sensing.
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