Abstract
This paper introduces the Direct Linear Discriminant Analysis (D-LDA) algorithm for feature extraction to reduce noise and redundant location information of the access points (APs) signals in wireless LAN (WLAN) indoor positioning system. Feature database is obtained by deploying D-LDA algorithm to extract the low-dimensional and discriminative positioning features from the original WLAN signal database. The dimensionality of the extracted features may be chosen by setting appropriate retained eigenvalues ratio of between-class scatter matrix. Based on the generated feature database, three typical localization algorithms including weighted k-nearest neighbor (WKNN), nearest-neighbor (NN) and maximum likelihood (ML) are carried for real-time positioning and the results are compared. D-LDA feature extraction algorithm obtains the higher accuracy than traditional localization algorithms while reducing the storage and computation cost significantly.
Foundation Item: This work was supported by the National Natural Science Foundation of China (Granted Nos. 61301132, 61300188, and 61301131), Natural Science Foundation of Liaoning Province of China No. 201602073, and the Fundamental Research Funds for the Central Universities Nos. 3132017129 and 3132016347.
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Yu, J., Deng, Z., Liu, X., Chen, J., Na, Z. (2019). WLAN Indoor Positioning Based on D-LDA Feature Extraction Algorithm. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_336
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DOI: https://doi.org/10.1007/978-981-10-6571-2_336
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