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- W4385800924 abstract "Smartphone based indoor localization has been widely explored for meeting the demand of high-precision low-cost indoor localization. Previous methods focus mainly on improving the localization accuracy of single sensor based localization, which may hinder their applications. In this article, we propose a novel encoder-decoder architecture for high-precision low-cost indoor localization. We first leverage two modal-specific encoders for feature extraction. Then, we propose two feature-level fusion strategies for feature fusion. Finally, we leverage two task-specific decoders for both position and orientation prediction. During training, we adopt WiFi-aided learning to provide a more reliable label. We test the proposed method in the corridor environment of a typical building. Experiments results show that our method can achieve less than a half meter localization accuracy, and meanwhile enjoys the run-time efficiency." @default.
- W4385800924 created "2023-08-15" @default.
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- W4385800924 date "2023-06-01" @default.
- W4385800924 modified "2023-10-16" @default.
- W4385800924 title "Hybrid Cascaded and Feature-Level Fusion Scheme for Multi-Modal Indoor Localization" @default.
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- W4385800924 doi "https://doi.org/10.1109/vtc2023-spring57618.2023.10200486" @default.
- W4385800924 hasPublicationYear "2023" @default.
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