Abstract
Kernel fuzzy K-means (KFKM) clustering algorithm is widely used to manage the fingerprint database for WiFi indoor positioning system to reduce the computational complexity of the position matching process. In this paper, we propose a novel WiFi positioning scheme based on KFKM algorithm, which can achieve a better precision by further optimizing the parameters employed in KFKM. Our proposed scheme consists of three steps. First, we choose an interval of reference points (RP) to build the fingerprint database. Then we decide an appropriate number of clusters based on the structure characteristics of fingerprint database using sample density method. During the process of clustering, we optimize the kernel parameter by approximating actual kernel matrix to a hypothetical ideal kernel matrix to improve the positioning precision. Through simulation results, we show that compared with the existing KFKM algorithm, our proposed scheme achieves 23.48% improvement in terms of positioning accuracy.
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References
Suroso, D.J., Cherntanomwong, P., Sooraksa, P., et al.: Location fingerprint technique using fuzzy C-means clustering algorithm for indoor localization. In: TENCON 2011 IEEE Region 10th Conference, pp. 88–92. IEEE, Bali (2011)
Saadi, M., Ahmad, T., Zhao, Y., et al.: An LED based indoor localization system using k-means clustering. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 246–252. IEEE (2016)
Sadeghi, H., et al.: A weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras. In: Sensor Array and Multichannel Signal Processing Workshop (2014)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Li, Z., Weida, Z.: Kernel clustering algorithm. Chin. J. Comput. 25(6), 587–590 (2002)
Yang, H.L., Huang, Z.G., Liu, J.W., et al.: WIFI fingerprint localization based on Kernel Fuzzy C-means clustering. J. Zhejiang Univ. 50, 1126–1133 (2016)
Acknowledgement
This work was supported by the National Natural Science foundation of China (61421062) and Hisense Co., Ltd.
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Hu, Z., Ma, L., Liu, B., Zhang, Z. (2018). Parameters Optimization for KFKM Clustering Algorithm Based on WiFi Indoor Positioning. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_34
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DOI: https://doi.org/10.1007/978-3-319-74521-3_34
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