Abstract
Indoor positioning using location fingerprints, which are received signal strength (RSS) from wireless access points (APs), has become a hot research topic during the last a few years. Traditional pattern classification based fingerprinting localization methods suffer high computational burden and require a large number of classifiers to determine the object location. To handle this problem, axial-decoupled indoor positioning based on location-fingerprints is proposed in this paper. The purpose is to reduce the decision complexity while keeping localization accuracy through computing the position on X- and Y-axis independently. First, the framework of axial-decoupled indoor positioning using location fingerprints is given. Then, the training and decision process of the proposed axial-decoupled indoor positioning is described in detail. Finally, pattern classifiers including the least squares support vector machine (LS-SVM), support vector machine (SVM) and traditional k-nearest neighbors (K-NN) are adopted and embedded in the proposed framework. Experimental results illustrate the effectiveness of the proposed axial-decoupled positioning method.
This work was supported by the National Science Foundation (NNSF) of China (under Grant 61100140 and 61104210) and the Construct Program of the Key Discipline in Hunan Province.
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This work is supported by the National Natural Science Foundation of China (No. 61100140 and 61104210).
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Yanhua, W., Yan, Z., Dongli, W., Xianbing, W. (2016). Axial-Decoupled Indoor Positioning Based on Location Fingerprints. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_2
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