UWB Indoor Localization Using Deep Learning LSTM Networks
Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/18/6290 |
| _version_ | 1797554117029134336 |
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| author | Alwin Poulose Dong Seog Han |
| author_facet | Alwin Poulose Dong Seog Han |
| author_sort | Alwin Poulose |
| collection | DOAJ |
| description | Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches. |
| first_indexed | 2024-03-10T16:26:12Z |
| format | Article |
| id | doaj.art-289e4ad2baf545a7bae406cec24da2d5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:26:12Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-289e4ad2baf545a7bae406cec24da2d52023-11-20T13:11:11ZengMDPI AGApplied Sciences2076-34172020-09-011018629010.3390/app10186290UWB Indoor Localization Using Deep Learning LSTM NetworksAlwin Poulose0Dong Seog Han1School of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaLocalization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.https://www.mdpi.com/2076-3417/10/18/6290Indoor localizationultra-wide band (UWB) signalstime of arrival (TOA)deep learninglong short-term memory (LSTM)trilateration algorithm |
| spellingShingle | Alwin Poulose Dong Seog Han UWB Indoor Localization Using Deep Learning LSTM Networks Applied Sciences Indoor localization ultra-wide band (UWB) signals time of arrival (TOA) deep learning long short-term memory (LSTM) trilateration algorithm |
| title | UWB Indoor Localization Using Deep Learning LSTM Networks |
| title_full | UWB Indoor Localization Using Deep Learning LSTM Networks |
| title_fullStr | UWB Indoor Localization Using Deep Learning LSTM Networks |
| title_full_unstemmed | UWB Indoor Localization Using Deep Learning LSTM Networks |
| title_short | UWB Indoor Localization Using Deep Learning LSTM Networks |
| title_sort | uwb indoor localization using deep learning lstm networks |
| topic | Indoor localization ultra-wide band (UWB) signals time of arrival (TOA) deep learning long short-term memory (LSTM) trilateration algorithm |
| url | https://www.mdpi.com/2076-3417/10/18/6290 |
| work_keys_str_mv | AT alwinpoulose uwbindoorlocalizationusingdeeplearninglstmnetworks AT dongseoghan uwbindoorlocalizationusingdeeplearninglstmnetworks |