NLOS Identification and Mitigation with WiFi: A Performance Evaluation in Dynamic Environments

Indoor-based navigation or positioning technology plays a key role in ensuring efficient navigation of autonomous robotic systems and persons in industrial environments and has been an active research field for the past decades. One key challenge for efficient and effective positioning in indoor settings is the prevalent presence of Non-Line-Of-Sight (NLOS) components or signals, coupled with the dynamic nature of industrial settings. Different solutions have been suggested in literature, to effectively identify and mitigate NLOS signals in WiFi positioning/navigation systems to meliorate positioning in industrial environments. However, the performance of these proposed approaches on par in multiple distinct environments is lacking in literature. In this paper, we compare the performance of recent WiFi-based NLOS identification and mitigation approaches on par for two distinct environments to ascertain two key insights: a) how the performance of proposed approaches changes based on the environment type; b) the impact of the environment type on NLOS identification and mitigation.