Parametric estimation for indoor localization application
This report shows the parametric estimation of Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) paths for localization. The parametric estimation of the LOS and NLOS paths like Time of Arrival (TOA) and Angle of Arrival (AOA) using Space-Alternating Generalized Expectation (SAGE) algorithm are expla...
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sg-ntu-dr.10356-681342023-07-07T17:21:05Z Parametric estimation for indoor localization application Hairul bin Anuwar Tan Soon Yim School of Electrical and Electronic Engineering DRNTU::Engineering This report shows the parametric estimation of Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) paths for localization. The parametric estimation of the LOS and NLOS paths like Time of Arrival (TOA) and Angle of Arrival (AOA) using Space-Alternating Generalized Expectation (SAGE) algorithm are explained. The experiment set up and execution will then be shared. Subsequently, the data obtained from the experiment will be processed using MATLAB software to produce the estimated TOA and AOA. With the estimated TOA and AOA found, the LOS and NLOS path can be known and used for localization. The errors of the measured values were then compared to the actual values to evaluate the accuracy of the parameter estimation. The errors of localization using LOS and NLOS will also be shown. Lastly are the future works that can be implemented to achieve better parameter estimation for localization. Bachelor of Engineering 2016-05-24T06:47:20Z 2016-05-24T06:47:20Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68134 en Nanyang Technological University 56 p. application/pdf |
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DRNTU::Engineering Hairul bin Anuwar Parametric estimation for indoor localization application |
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This report shows the parametric estimation of Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) paths for localization. The parametric estimation of the LOS and NLOS paths like Time of Arrival (TOA) and Angle of Arrival (AOA) using Space-Alternating Generalized Expectation (SAGE) algorithm are explained. The experiment set up and execution will then be shared. Subsequently, the data obtained from the experiment will be processed using MATLAB software to produce the estimated TOA and AOA. With the estimated TOA and AOA found, the LOS and NLOS path can be known and used for localization. The errors of the measured values were then compared to the actual values to evaluate the accuracy of the parameter estimation. The errors of localization using LOS and NLOS will also be shown. Lastly are the future works that can be implemented to achieve better parameter estimation for localization. |
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Tan Soon Yim |
author_facet |
Tan Soon Yim Hairul bin Anuwar |
format |
Final Year Project |
author |
Hairul bin Anuwar |
author_sort |
Hairul bin Anuwar |
title |
Parametric estimation for indoor localization application |
title_short |
Parametric estimation for indoor localization application |
title_full |
Parametric estimation for indoor localization application |
title_fullStr |
Parametric estimation for indoor localization application |
title_full_unstemmed |
Parametric estimation for indoor localization application |
title_sort |
parametric estimation for indoor localization application |
publishDate |
2016 |
url |
http://hdl.handle.net/10356/68134 |
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1772825331170803712 |