Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 1; p. 125
• Main Authors: Zhang, Lingwen, Xiao, Ning, Yang, Wenkao, Li, Jun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02-01-2019; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Fingerprinting; Global positioning systems; GPS; heterogeneous features fusion (HFF); indoor localization; International conferences; Internet of Things; Kalman filters; Localization; Location based services; Machine learning; Methods; optimization; Propagation; Signal strength; Beijing China; New York; United States > US; China; Japan; indoor localization; machine learning; optimization; heterogeneous features fusion (HFF)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19010125

In the era of the Internet of Things and Artificial Intelligence, the Wi-Fi fingerprinting-based indoor positioning system (IPS) has been recognized as the most promising IPS for various applications. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods. However, currently methods are based on single-feature Received Signal Strength (RSS), which is extremely unstable in performance in terms of precision and robustness. The reason for this is that single feature machines cannot capture the complete channel characteristics and are susceptible to interference. The objective of this paper is to exploit the Time of Arrival (TOA) feature and propose a heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Several challenges are addressed: (1) machine learning models based on heterogeneous features, (2) the optimization of algorithms for high precision and robustness, and (3) computational complexity. This paper provides several heterogeneous features fusion-based localization models. Their effectiveness and efficiency are thoroughly compared with state-of-the-art methods.