UWB NLOS/LOS Classification Using Deep Learning Method
Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path de...
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| Veröffentlicht in: | IEEE communications letters Jg. 24; H. 10; S. 2226 - 2230 |
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| Sprache: | Englisch |
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IEEE
01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the "clean" channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance. |
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| AbstractList | Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the “clean” channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance. |
| Author | Chen, Yuwei Liu, Di Bo, Yuming Shen, Jichun Jiang, Changhui Chen, Shuai |
| Author_xml | – sequence: 1 givenname: Changhui orcidid: 0000-0002-4788-2464 surname: Jiang fullname: Jiang, Changhui email: chagnhui.jiang1992@gmail.com organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Jichun surname: Shen fullname: Shen, Jichun email: s365445689@hotmail.com organization: Hesai Technology, Building L2-B, Hongqiao World Centre, Shanghai, China – sequence: 3 givenname: Shuai surname: Chen fullname: Chen, Shuai email: chenshuai@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 4 givenname: Yuwei orcidid: 0000-0003-0148-3609 surname: Chen fullname: Chen, Yuwei email: yuwei.chen@nls.fi organization: Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, Finland – sequence: 5 givenname: Di orcidid: 0000-0003-4528-9605 surname: Liu fullname: Liu, Di email: liudinust@163.com organization: School of Automation, Nanjing Institute of Technology, Nanjing, China – sequence: 6 givenname: Yuming surname: Bo fullname: Bo, Yuming email: byming@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China |
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| SubjectTerms | Artificial neural networks Channels Classification CNN Convolution Deep learning Feature extraction Impulse response Indoor environments IP networks Kernel Line of sight Logic gates LSTM Machine learning Multilayers NLOS Position errors Signal classification Support vector machines Ultrawideband UWB |
| Title | UWB NLOS/LOS Classification Using Deep Learning Method |
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