Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning
Multipath errors caused by signal reflection are challenging to correct when compared to other errors in radio navigation. Specifically, multipath error for indoor positioning is a major error because various materials that surround the environment can cause errors through reflected signals. This st...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10966902/ |
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| author | Min-Ji Kim O-Jong Kim Jikang Lee Changdon Kee Juhyun Maeng |
| author_facet | Min-Ji Kim O-Jong Kim Jikang Lee Changdon Kee Juhyun Maeng |
| author_sort | Min-Ji Kim |
| collection | DOAJ |
| description | Multipath errors caused by signal reflection are challenging to correct when compared to other errors in radio navigation. Specifically, multipath error for indoor positioning is a major error because various materials that surround the environment can cause errors through reflected signals. This study focuses on mitigating multipath in single-transmitter-based indoor positioning, which employs a transmitter emitting multi-channel signals and uses carrier-phase measurements for precise positioning with low noise. Owing to the difficulty in accurately modelling reflected signals mathematically, this study proposes a neural-network-based technique to detect and categorize multipath-distorted signals using short-time Fourier transform (STFT) to extract features in the time-frequency domain. The STFT magnitudes in the spectrogram are used to train and validate the neural networks that detect and isolate multipath-distorted measurements to minimize positioning errors. This paper also presents a novel method that employs a trained neural network to classify the most contaminated signals by considering a single-transmitter-based positioning system with an antenna array. Experiments conducted under challenging indoor conditions demonstrate an approximately 30% reduction in the positioning error after applying the proposed multipath mitigation methods. |
| format | Article |
| id | doaj-art-983e34d2242e4c95a3ace79a840f8b4a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-983e34d2242e4c95a3ace79a840f8b4a2025-08-20T03:14:13ZengIEEEIEEE Access2169-35362025-01-0113681276814010.1109/ACCESS.2025.356129010966902Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise PositioningMin-Ji Kim0https://orcid.org/0009-0003-2928-0456O-Jong Kim1https://orcid.org/0000-0003-0752-6219Jikang Lee2https://orcid.org/0009-0006-8858-5374Changdon Kee3https://orcid.org/0000-0002-8691-7068Juhyun Maeng4https://orcid.org/0000-0003-0594-7554Department of Aerospace Engineering, Sejong University, Seoul, South KoreaDepartment of Aerospace Engineering, Sejong University, Seoul, South KoreaDepartment of Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Aerospace Engineering, Seoul National University, Seoul, South KoreaKorea Aerospace Research Institute, Daejeon, South KoreaMultipath errors caused by signal reflection are challenging to correct when compared to other errors in radio navigation. Specifically, multipath error for indoor positioning is a major error because various materials that surround the environment can cause errors through reflected signals. This study focuses on mitigating multipath in single-transmitter-based indoor positioning, which employs a transmitter emitting multi-channel signals and uses carrier-phase measurements for precise positioning with low noise. Owing to the difficulty in accurately modelling reflected signals mathematically, this study proposes a neural-network-based technique to detect and categorize multipath-distorted signals using short-time Fourier transform (STFT) to extract features in the time-frequency domain. The STFT magnitudes in the spectrogram are used to train and validate the neural networks that detect and isolate multipath-distorted measurements to minimize positioning errors. This paper also presents a novel method that employs a trained neural network to classify the most contaminated signals by considering a single-transmitter-based positioning system with an antenna array. Experiments conducted under challenging indoor conditions demonstrate an approximately 30% reduction in the positioning error after applying the proposed multipath mitigation methods.https://ieeexplore.ieee.org/document/10966902/Indoor positioningmultipath mitigationmachine learningneural networkshort-time Fourier transformtime-frequency analysis |
| spellingShingle | Min-Ji Kim O-Jong Kim Jikang Lee Changdon Kee Juhyun Maeng Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning IEEE Access Indoor positioning multipath mitigation machine learning neural network short-time Fourier transform time-frequency analysis |
| title | Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning |
| title_full | Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning |
| title_fullStr | Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning |
| title_full_unstemmed | Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning |
| title_short | Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning |
| title_sort | time frequency analysis and neural network based multipath mitigation method for precise positioning |
| topic | Indoor positioning multipath mitigation machine learning neural network short-time Fourier transform time-frequency analysis |
| url | https://ieeexplore.ieee.org/document/10966902/ |
| work_keys_str_mv | AT minjikim timefrequencyanalysisandneuralnetworkbasedmultipathmitigationmethodforprecisepositioning AT ojongkim timefrequencyanalysisandneuralnetworkbasedmultipathmitigationmethodforprecisepositioning AT jikanglee timefrequencyanalysisandneuralnetworkbasedmultipathmitigationmethodforprecisepositioning AT changdonkee timefrequencyanalysisandneuralnetworkbasedmultipathmitigationmethodforprecisepositioning AT juhyunmaeng timefrequencyanalysisandneuralnetworkbasedmultipathmitigationmethodforprecisepositioning |