Multiple Similarity Analysis-Based Deep Metric Learning for Enhancing Wi-Fi Fingerprint Indoor Localization

Wi-Fi RSSI fingerprint localization, one of the more mature solutions for indoor localization, is active in Internet of Things (IoT) applications. However, there are still some challenges to learning RSSI features. Although recent methods utilize deep metric learning techniques to discover the hidden correspondences between RSSI features and physical spatial locations, because the currently used deep metric learning methods focus more on the positive similarity (Similarity-P) of RSSI features, ignoring the constraints imposed by self-similarity (Similarity-S) and negative similarity (Similarity-N) on the model and having to ternary group RSSIs in advance. We propose a deep metric learning indoor localization method based on multiple similarities, constructed with a multiple similarity loss function in which Similarity-S pair mining of RSSI features is performed to mine positive and negative pair sets, which are then pair weighting using Similarity-P and Similarity-N. Finally, RSSI features are extracted and localized online using the WKNN method. Experiment results confirm that our proposed method can achieve excellent positioning performance with few-shot data sets.