Enhancing Trust in Localization Systems: An XAI Approach

Abstract

Machine learning and deep learning are becoming more popular in the area of indoor localization leveraging the Wi-Fi Received signal strength demonstrating high performance and scalability. However, such models usually perform poorly in noisy environments, leading to reduced trust in their reliability. In this work, we propose a novel framework that enhances trust in AI-based localization by using explainable AI techniques for automatic real time auditing of model behavior. Our approach detects and corrects any unusual behavior on-the-fly. Experiments in a testbed and two challenging real-world settings demonstrate that our framework effectively stabilizes performance and maintains trust in the localization models.