Improving In-Building Asset Localization by Offset Vector and Convergence Calibration Methods

Abstract

Accessibility of assets impacts the efficiency of various work processes during building operations. An effective in-building asset localization solution that can locate assets accurately is needed to improve the efficiency of building asset management. Multipath and fading effects caused by the complexity of buildings make it challenging to locate building assets (e.g., equipment, components, tools) by radio frequency identification (RFID)-based indoor location solutions. This study tests two calibration methods, namely, the offset vector method (OVM) and convergence method (CM), to mitigate multipath and fading effects in indoor environments to support in-building asset management. A RFID-based indoor asset localization solution is tested in two building-scale test beds with different complexities and functions. The proposed solution locates assets by utilizing a virtual-tag-enabled (VTE) algorithm, and two calibration methods are integrated into the VTE algorithm to improve the location accuracy. The solution is deployed in a warehouse building and an office building and assessed through a series of field tests. The tests report an average accuracy of