Abstract
At present, the traditional indoor localization system based on Received Signal Strength (RSS) cannot provide high efficiency, high precision and high adaptability due to the instability of the RSS. For improving the locating problem preferably, Channel State Information (CSI) replacing RSS as a more fine-grained signal. However, its merits of perception of the surrounding environment are also its defects, not all CSI raw data can be fully qualified for positioning. In this paper, we proposed a novel method of Wi-Fi indoor localization. At first, the localization system assembles the unified feature dataset after channel combination processing. then it uses a two-stage method, Principal Component Analysis (PCA) plus Spearman Rank Correlation Coefficient (SRCC) to select and compress the sub-sampled CSI data. Finally, it adopts Weighted K nearest neighbor (WKNN) classifier to recognize the corresponding physical position. The experimental verification results show that proposed method, which named PCA-CC, compared with other RSS-based location systems, can improve the accuracy of the positioning results in real environment effectively.
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Dang, X., Ren, J., Hao, Z., Yan, Y., Hei, Y. (2019). The Improvement of Indoor Localization Precision Through PCA-Based Channel Combination Method. In: Shen, S., Qian, K., Yu, S., Wang, W. (eds) Wireless Sensor Networks. CWSN 2018. Communications in Computer and Information Science, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-13-6834-9_10
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DOI: https://doi.org/10.1007/978-981-13-6834-9_10
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