MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 5; p. 599
• Main Authors: Liu, Wen, Jia, Mingjie, Deng, Zhongliang, Qin, Changyan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.04.2022; MDPI
• Subjects: Accuracy; Algorithms; channel state information; Deep learning; effective CSI; Feature extraction; fingerprint localization; Global positioning systems; GPS; Information retrieval; Localization; Methods; multi-head self-attention; Neural networks; Outdoor activities; Signal processing; State (computer science); Support vector machines; channel state information; effective CSI; multi-head self-attention; fingerprint localization; feature extraction
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24050599

Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory.