InPLaCE RFID: Indoor Path Loss Translation for Object Localization in Cluttered Environments

Radio Frequency Identification (RFID) is widely used in indoor positioning systems for object tracking and localization. However, there are several challenges that are yet to be addressed especially in 3-D space. When objects are cluttered densely, an arrangement synonymous to that of heaps of haphazardly arranged files in an office, locating and retrieving a file manually from the heap becomes a laborious task. In this paper, we address the challenge of localization and retrieval of objects in cluttered environments using passive RFID tags, by developing a novel indoor path loss translational model that considers the signal properties across the clutter. The proposed InPLaCE RFID system estimates the position of the object within a clutter by employing a robust translation model that accounts for the properties of the clutter and helps compensate for estimation errors over existing path loss models. Our experiments over different cluttered environments show that the proposed translational model improves the localization accuracy of objects over existing path loss models.