Scholarlink

<p>Visible Light Positioning (VLP) is an emerging indoor positioning technology that utilizes the Received Signal Strengths (RSSs) of Light Emitting Diodes (LEDs) to calculate a receiver’s location. Unlike traditional radio-frequency-based positioning solutions, VLP is immune to electromagnetic interference, making it particularly valuable in areas such as hospitals, smart homes, and industrial automation. However, one of the most notable challenges in real VLP implementations is the dynamic behavior of illumination particularly due to light dimming which significantly affects RSS measurements and, consequently, increases the localization error. This paper examines the impact of light dimming on VLP performance by analyzing machine-learning (ML) regression models that predict three-dimensional position coordinates from dimming-affected RSS values. Four regression models Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gaussian Process Regression (GPR) are trained and tested under dimming factors <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\alpha\:\in\:\:\left\{0.2,\:0.4,\:0.6,\:0.8,\:1.0\right\}\)</EquationSource> </InlineEquation> in a simulated indoor environment. Localization performance is quantified using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in meters. The results show that GPR consistently provides the most accurate and robust positioning across all dimming levels, achieving <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:MAE=0.15\:m\)</EquationSource> </InlineEquation> at <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:\alpha\:=0.2\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\:MAE=0.14\:m\)</EquationSource> </InlineEquation> at <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\:\alpha\:=0.8-1.0\)</EquationSource> </InlineEquation>, while maintaining <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\:RMSE=0.36\:m\)</EquationSource> </InlineEquation> at <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\:\alpha\:=0.2\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\:RMSE=0.35\:m\)</EquationSource> </InlineEquation> for <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\:\alpha\:=0.4-1.0\)</EquationSource> </InlineEquation>. These findings indicate that GPR is a reliable approach for VLP systems operating under light dimming and fluctuating illumination conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Topic: regression-based machine learning approaches for visible light positioning under light dimming conditions

  • 期刊论文

  • 发表日期：

    2026年4月7日

查看全文
  • M. W. P. Maduranga,
  • Poompat Saengudomlert,
  • Vindula L. Jayaweera,
  • Nishan Dharmaweera

摘要

Visible Light Positioning (VLP) is an emerging indoor positioning technology that utilizes the Received Signal Strengths (RSSs) of Light Emitting Diodes (LEDs) to calculate a receiver’s location. Unlike traditional radio-frequency-based positioning solutions, VLP is immune to electromagnetic interference, making it particularly valuable in areas such as hospitals, smart homes, and industrial automation. However, one of the most notable challenges in real VLP implementations is the dynamic behavior of illumination particularly due to light dimming which significantly affects RSS measurements and, consequently, increases the localization error. This paper examines the impact of light dimming on VLP performance by analyzing machine-learning (ML) regression models that predict three-dimensional position coordinates from dimming-affected RSS values. Four regression models Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gaussian Process Regression (GPR) are trained and tested under dimming factors \(\:\alpha\:\in\:\:\left\{0.2,\:0.4,\:0.6,\:0.8,\:1.0\right\}\) in a simulated indoor environment. Localization performance is quantified using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in meters. The results show that GPR consistently provides the most accurate and robust positioning across all dimming levels, achieving \(\:MAE=0.15\:m\) at \(\:\alpha\:=0.2\) and \(\:MAE=0.14\:m\) at \(\:\alpha\:=0.8-1.0\) , while maintaining \(\:RMSE=0.36\:m\) at \(\:\alpha\:=0.2\) and \(\:RMSE=0.35\:m\) for \(\:\alpha\:=0.4-1.0\) . These findings indicate that GPR is a reliable approach for VLP systems operating under light dimming and fluctuating illumination conditions.