Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightIn addition, while the proposed HDOP technique provides localization for all of the test points, both the NLR and INR algorithms perform below the expected levels at the same points.
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Özet
Ultra-wideband (UWB) technology provides considerable performance in many indoor localization problems thanks to its very wide spectrum and high-resolution characteristics. In this paper, we propose using the horizontal dilution of precision (HDOP) to decrease the localization error in indoor environments for UWB localization systems. To achieve this aim, first we determine the positioning accuracy of a commercially available UWB positioning system using laboratory experiments. Next, the results of the position estimations obtained by the HDOP are compared with the experimental results acquired by the UWB positioning system. Finally, we investigate a detailed comparison with the least squares (LS), nonlinear regression (NLR), and iterative nonlinear regression (INR) techniques. In terms of the mean position estimation error, the proposed HDOP technique increases the performance of the UWB positioning system and the LS algorithm by approximately 10% and 3%, respectively. In addition, while the proposed HDOP technique provides localization for all of the test points, both the NLR and INR algorithms perform below the expected levels at the same points.
Abstract
Ultra-wideband (UWB) technology provides considerable performance in many indoor localization problems thanks to its very wide spectrum and high-resolution characteristics. In this paper, we propose using the horizontal dilution of precision (HDOP) to decrease the localization error in indoor environments for UWB localization systems. To achieve this aim, first we determine the positioning accuracy of a commercially available UWB positioning system using laboratory experiments. Next, the results of the position estimations obtained by the HDOP are compared with the experimental results acquired by the UWB positioning system. Finally, we investigate a detailed comparison with the least squares (LS), nonlinear regression (NLR), and iterative nonlinear regression (INR) techniques. In terms of the mean position estimation error, the proposed HDOP technique increases the performance of the UWB positioning system and the LS algorithm by approximately 10% and 3%, respectively. In addition, while the proposed HDOP technique provides localization for all of the test points, both the NLR and INR algorithms perform below the expected levels at the same points.