Abstract
Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan.
Highlights
Wi-Fi received signal strength indicator (RSSI) is one of the basic sensory observations widely used for indoor localization
Because a detailed description about the particle filter can be found in many works [31,32,33], this paper describes how that information with the learning-based localization can be combined in the particle filter framework
This paper proposes a new semi-supervised learning algorithm by combining core concepts of Laplacian embedded regression least square (LapERLS)’s pseudolabeling, time-series learning, and Laplacian Least Square (LapLS)-based balancing optimization
Summary
Wi-Fi received signal strength indicator (RSSI) is one of the basic sensory observations widely used for indoor localization. Adding a large amount of unlabeled data in the semi-supervised learning framework can prevent the decrement in localization accuracy when using a small amount of labeled data. In the pseudolabeling process, the time-series graph Laplacian SVM optimization is constructed, which estimates labels of the unlabeled data. Because the pseudolabels are used as the learning input of LapLS, the proposed optimization becomes a linear problem that can be solved fast In this learning process, two decoupled balancing parameters are individually weighed to the labeled term and the pseudolabeled term, separately, which makes it simple to balance the labeled and the pseudolabeled data. According to the variation of the number of the labeled training data and the Wi-Fi access points, the proposed algorithm gives the best localization performance without sacrificing the computation time.
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