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- W3204205940 abstract "In ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The machine learning-based LOS/NLOS identification methods have been widely used due to its advantages of eliminating the need for prior knowledge. Unfortunately, traditional identification methods, such as LS-SVM, DT, and KTT, decrease significantly in other scenarios. Fortunately, Capsule Networks (CapsNet) showed its advantages of high accuracy and robustness in image classification as one of the latest machine learning achievements. Recently, some researchers noticed CapsNet and extended its application to LOS/NLOS identification. However, the generation ability and robustness of the capsule network needs to be further studied. In this paper, we verify the generalization ability and robustness of CapsNet based on the IEEE 802.15.4a channel model and experimental data. Experimental results show that the generation ability and robustness of the CapsNet-based method is better than the benchmark methods." @default.
- W3204205940 created "2021-10-11" @default.
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- W3204205940 date "2021-01-01" @default.
- W3204205940 modified "2023-09-24" @default.
- W3204205940 title "Performance Verification of Capsule Networks in LOS/NLOS Identification for UWB Positioning" @default.
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- W3204205940 doi "https://doi.org/10.1007/978-981-16-3142-9_53" @default.
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