Ensemble-Based Learning In Indoor Localization: A Hybrid Approach
2019 IEEE 90TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-FALL)(2019)
摘要
In this paper, we are concerned with indoor localization based on multiple-antenna channel measurements. Indoor localization is an active area of research due to its great importance in many applications. We propose a hybrid algorithm which combines the benefits of two techniques, namely signal processing and machine learning. We validate our algorithm based on real measurements acquired from two practical setups. Our approach shows a very promising performance in the IEEE CTW 2019 Positioning Algorithm Competition where the algorithm achieves an accuracy within RMSE values below 10 cm. We further build a setup in another indoor environment, where the algorithm still proves a very good performance compared to state-of-the art techniques used in indoor localization tasks.
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关键词
hybrid algorithm,signal processing,machine learning,indoor environment,indoor localization,IEEE CTW 2019,positioning algorithm competition,ensemble-based learning,multiple-antenna channel measurements,RMSE values,indoor localization tasks
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