Abstract
Traditional Time of Flight (TOF) estimation methods [1] are inaccurate in multipath and noisy environments. This paper enhances TOF measurement with the Multiple Signal Classification (MUSIC) algorithm [2]. It uses spatial smoothing on channel state information (CSI) dataset, frequency domain analysis and MUSIC-based methods to distinguish signals, and the least squares method to reduce multipath-induced errors for transmitter positioning. It also conducts linear fitting, maximum likelihood estimation, and statistics to evaluate and reduce TOF errors. The paper achieves precise positioning in multipath scenarios and improves the MUSIC algorithm’s accuracy and applicability under noisy conditions by analyzing error sources.
This paper is selected from the “Spark Cup” Joint Competition of Mathematical Modeling, with an acceptance of 7/514 teams . The authors dealed with Problem B “Indoor Positioning Problem Based on WiFi” with a real-world dataset, which has been provieded by Prof. Xiaoping Li and Prof. Siyi Qin from University of Electronic Science and Technology of China. This team then converted their competition report into an LNCS-style paper for the Competition Workshop of COCOON 2024.
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Wang, R., Ren, H., Jia, Q. (2025). Indoor Localization Problem Based on WiFi Using MUSIC Algorithm and Maximum Likelihood Estimation. In: Chen, Y., Gao, X., Sun, X., Zhang, A. (eds) Computing and Combinatorics. COCOON 2024. Lecture Notes in Computer Science, vol 15163. Springer, Singapore. https://doi.org/10.1007/978-981-96-1195-9_10
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DOI: https://doi.org/10.1007/978-981-96-1195-9_10
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