A Fingerprint Dictionary Processing Approach in Indoor Localization System Based on Wi-Fi

Abstract

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage and bandwidth requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.