Enhancing Fingerprinting Indoor Positioning Systems Through Hierarchical Clustering and GAN-Based CNN

The importance of locating objects or people indoors has grown due to the need for asset tracking, worker location in industrial settings, and monitoring passenger flow in transportation hubs. Machine Learning (ML) can enhance the accuracy of indoor positioning systems (IPS) and simplify the laborious offline phase of fingerprinting. Our study aims to explore this potential. We propose dividing the indoor environment into zones using Hierarchical Clustering Analysis (HCA) and applying data augmentation (DA) through Generative Adversarial Networks (GANs) and Convolutional Neural Network (CNN) on transformed RSSI measurements. Converting digital entries into images enables better exploration and analysis of complex data, revealing patterns and relationships in radio properties. We demonstrate the effectiveness and accuracy of this novel IPS approach using a dataset from Western Michigan University's Waldo Library [Moh+18]. Our results show that this approach excels in relatively large indoor environments.