Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi
Various research works have been proposed for Wi-Fi-based indoor localization, including Received Signal Strength Indicator (RSSI)-based fingerprint algorithm, Angle of Arrival (AoA)-based algorithm and so on. However, since RSSI value cannot accurately express the spatial features of emitted wirele...
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| Veröffentlicht in: | Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2019-10, Vol.162, p.106864, Article 106864 |
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| creator | Zhao, Bobai Zhu, Dali Xi, Tong Jia, Chenggang Jiang, Shang Wang, Siye |
| description | Various research works have been proposed for Wi-Fi-based indoor localization, including Received Signal Strength Indicator (RSSI)-based fingerprint algorithm, Angle of Arrival (AoA)-based algorithm and so on. However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. In the real indoor environment, our proposed algorithm improves the positioning accuracy up to 22% in LoS scenario, and 9.8% in NLoS scenario, respectively. |
| doi_str_mv | 10.1016/j.comnet.2019.106864 |
| format | Article |
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However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. 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However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. In the real indoor environment, our proposed algorithm improves the positioning accuracy up to 22% in LoS scenario, and 9.8% in NLoS scenario, respectively.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.comnet.2019.106864</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Computer networks (Amsterdam, Netherlands : 1999), 2019-10, Vol.162, p.106864, Article 106864 |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Accuracy Adaptive filters Algorithms Angle of arrival Artificial neural networks Bayesian analysis Channel State Information Computer simulation Convolution neural network Fingerprints Indoor environments Indoor localization Kalman filters Localization Neural networks Noise Noise measurement Position measurement Signal distortion Signal strength Variational bayes adaptive Kalman filter Wireless access points |
| title | Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi |
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