DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID

Abstract

Passive radio frequency identification (RFID) tags have been widely used in logistics, supply chain, warehousing, and other fields. However, for RFID-based automatic inventory management in warehouses, the deployment of current methods is more complex, and the localization range still has some limitations. This paper proposes DNCL, which is a hybrid passive RFID localization scheme based on direction-of-arrival (DOA) estimation and nonmetric multidimensional scaling (NMDS) to achieve spatial coordinate localization of tagged objects on shelves or racks using a single antenna for simple 1D scanning. DNCL uses antenna dynamic scanning to generate a virtual antenna array for the dynamic information capture of tags in the scene, which helps eliminate the phase shift produced by ambient noise. We apply the angle profile linear model to identify the characteristics of each tag and introduce the NMDS algorithm to improve the robustness of the scheme through the fixed layout of the reference tags, which can reliably estimate the coordinates of the tagged objects in the space. This paper realizes a prototype system and validated its practical performance in real complex situationse by COTS RFID devices. The results indicate that DNCL can achieve high accuracy for the localization of passive tags in free space.