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1. Data-driven Wireless Positioning

Author

Stahlke, Maximilian, Feigl, Tobias, Kram, Sebastian, Ott, Jonathan, Seitz, Jochen, Mutschler, Christopher, Mutschler, Christopher, editor, Münzenmayer, Christian, editor, Uhlmann, Norman, editor, and Martin, Alexander, editor

Published
2024
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2. Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Author

Kram, Sebastian, Kraus, Christopher, Feigl, Tobias, Stahlke, Maximilian, Robert, Jörg, and Mutschler, Christopher

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. State-of-the art CM fingerprinting(FP)-based methods employ convolutional neural networks (CNN) to extract the spatial information. However, they need spatially dense data sets (associated with high acquisition and maintenance efforts) to work well -- which is rarely the case in practical applications. If such data is not available (or its quality is low), we cannot compensate the performance degradation of CNN-based FP as they do not provide statistical position estimates, which prevents a fusion with other sources of information on the observation level. We propose a novel localization framework that adapts well to sparse datasets that only contain CMs of specific areas within the environment with strong multipath propagation. Our framework compresses CMs into informative features to unravel spatial information. It then regresses Gaussian processes (GPs) for each of them, which imply statistical observation models based on distance-dependent covariance kernels. Our framework combines the trained GPs with line-of-sight ranges and a dynamics model in a particle filter. Our measurements show that our approach outperforms state-of-the-art CNN fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a realistic industrial indoor environment., Comment: 10 pages, 8 figures

Published
2022

4. Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions

Author

Potortì, Francesco, primary, Crivello, Antonino, additional, Lee, Soyeon, additional, Vladimirov, Blagovest, additional, Park, Sangjoon, additional, Chen, Yushi, additional, Wang, Long, additional, Chen, Runze, additional, Zhao, Fang, additional, Zhuge, Yue, additional, Luo, Haiyong, additional, Perez-Navarro, Antoni, additional, Jiménez, Antonio R., additional, Wang, Han, additional, Liang, Hengyi, additional, De Cock, Cedric, additional, Plets, David, additional, Cui, Yan, additional, Xiong, Zhi, additional, Li, Xiaodong, additional, Ding, Yiming, additional, Franco, Fernando Javier Álvarez, additional, Polo, Fernando Jesús Aranda, additional, Rodríguez, Felipe Parralejo, additional, Moreira, Adriano, additional, Pendão, Cristiano, additional, Silva, Ivo, additional, Ortiz, Miguel, additional, Zhu, Ni, additional, Li, Ziyou, additional, Renaudin, Valérie, additional, Wei, Dongyan, additional, Ji, Xinchun, additional, Zhang, Wenchao, additional, Wang, Yan, additional, Ding, Longyang, additional, Kuang, Jian, additional, Zhang, Xiaobing, additional, Dou, Zhi, additional, Yang, Chaoqun, additional, Kram, Sebastian, additional, Stahlke, Maximilian, additional, Mutschler, Christopher, additional, Coene, Sander, additional, Li, Chenglong, additional, Venus, Alexander, additional, Leitinger, Erik, additional, Tertinek, Stefan, additional, Witrisal, Klaus, additional, Wang, Yi, additional, Wang, Shaobo, additional, Jin, Beihong, additional, Zhang, Fusang, additional, Su, Chang, additional, Wang, Zhi, additional, Li, Siheng, additional, Li, Shitao, additional, Pan, Mengguan, additional, Zheng, Wang, additional, Luo, Kai, additional, Ma, Ziyao, additional, Gao, Yanbiao, additional, Chang, Jiaxing, additional, Ren, Hailong, additional, Guo, Wenfang, additional, and Torres-Sospedra, Joaquín, additional

Published
2024
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5. Location-Aware Range-Error Correction for Improved UWB Localization.

Author

Coene, Sander, Li, Chenglong, Kram, Sebastian, Tanghe, Emmeric, Joseph, Wout, and Plets, David

Subjects
INFORMATION measurement, UNITS of measurement, REGRESSION analysis, PREDICTION models, AMPLITUDE estimation, CONFERENCES & conventions, LOCALIZATION (Mathematics)
Abstract

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Author

European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Japan Science and Technology Agency, Bavarian Ministry of Economy, Infrastructure, Transport and Technology, Fundação para a Ciência e a Tecnologia (Portugal), Ministry of Science and Technology (Taiwan), Slovak Academy of Sciences, Ministry of Education (Slovak Republic), National Natural Science Foundation of China, Government of Singapore, Japan Society for the Promotion of Science, National Key Research and Development Program (China), Potortì, Francesco, Torres-Sospedra, Joaquín, Quezada Gaibor, Darwin, Jiménez Ruiz, Antonio R., Seco Granja, Fernando, Pérez-Navarro, Antoni, Ortiz, Miguel, Zhu, Ni, Renaudin, Valerie, Ichikari, Ryosuke, Shimomura, Ryo, Plets, David, Opiela, Miroslav, Džama, Jakub, Zhang, Liqiang, Li, Hu, Chen, Boxuan, Liu, Yu, Yean, Seanglidet, Lim, Bo Zhi, Teo, Wei Jie, Lee, Bu Sung, Oh, Hong Lye, Ohta, Nozomu, Nagae, Satsuki, Kurata, Takeshi, Wei, Dongyan, Ji, Xinchun, Zhang, Wenchao, Kram, Sebastian, Stahlke, Maximilian, Mutschler, Christopher, Crivello, Antonino, Barsocchi, Paolo, Girolami, Michele, Palumbo, Filippo, Chen, Ruizhi, Wu, Yuan, Li, Wei, Yu, Yue, Xu, Shihao, Huang, Lixiong, Liu, Tao, Kuang, Jian, Niu, Xiaoji, Yoshida, Takuto, Nagata, Yoshiteru, Fukushima, Yuto, Fukatani, Nobuya, Hayashida, Nozomi, Asai, Yusuke, Urano, Kenta, Ge, Wenfei, Lee, Nien-Ting, Fang, Shih-Hau, Jie, You-Cheng, Young, Shawn-Rong, Chien, Ying-Ren, Yu, Chih-Chieh, Ma, Chengqi, Wu, Bang, Zhang, Wei, Wang, Yankun, Fan, Yonglei, Poslad, Stefan, Selviah, David R., Wang, Weixi, Yuan, Hong, Yonamoto, Yoshitomo, Yamaguchi, Masahiro, Kaichi, Tomoya, Zhou, Baoding, Liu, Xu, Gu, Zhining, Yang, Chengjing, Wu, Zhiqian, Xie, Doudou, Huang, Can, Zheng, Lingxiang, Peng, Ao, Jin, Ge, Wang, Qu, Luo, Haiyong, Xiong, Hao, Bao, Linfeng, Zhang, Pushuo, Zhao, Fang, Yu, Chia-An, Hung, Chun-Hao, Antsfeld, Leonid, Chidlovskii, Boris, Jiang, Haitao, Xia, Ming, Yan, Dayu, Li, Yuhang, Dong, Yitong, Silva, Ivo, Pendão, Cristiano, Meneses, Filipe, Nicolau, Maria João, Costa, António, Moreira, Adriano, De Cock, Cedric, European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Japan Science and Technology Agency, Bavarian Ministry of Economy, Infrastructure, Transport and Technology, Fundação para a Ciência e a Tecnologia (Portugal), Ministry of Science and Technology (Taiwan), Slovak Academy of Sciences, Ministry of Education (Slovak Republic), National Natural Science Foundation of China, Government of Singapore, Japan Society for the Promotion of Science, National Key Research and Development Program (China), Potortì, Francesco, Torres-Sospedra, Joaquín, Quezada Gaibor, Darwin, Jiménez Ruiz, Antonio R., Seco Granja, Fernando, Pérez-Navarro, Antoni, Ortiz, Miguel, Zhu, Ni, Renaudin, Valerie, Ichikari, Ryosuke, Shimomura, Ryo, Plets, David, Opiela, Miroslav, Džama, Jakub, Zhang, Liqiang, Li, Hu, Chen, Boxuan, Liu, Yu, Yean, Seanglidet, Lim, Bo Zhi, Teo, Wei Jie, Lee, Bu Sung, Oh, Hong Lye, Ohta, Nozomu, Nagae, Satsuki, Kurata, Takeshi, Wei, Dongyan, Ji, Xinchun, Zhang, Wenchao, Kram, Sebastian, Stahlke, Maximilian, Mutschler, Christopher, Crivello, Antonino, Barsocchi, Paolo, Girolami, Michele, Palumbo, Filippo, Chen, Ruizhi, Wu, Yuan, Li, Wei, Yu, Yue, Xu, Shihao, Huang, Lixiong, Liu, Tao, Kuang, Jian, Niu, Xiaoji, Yoshida, Takuto, Nagata, Yoshiteru, Fukushima, Yuto, Fukatani, Nobuya, Hayashida, Nozomi, Asai, Yusuke, Urano, Kenta, Ge, Wenfei, Lee, Nien-Ting, Fang, Shih-Hau, Jie, You-Cheng, Young, Shawn-Rong, Chien, Ying-Ren, Yu, Chih-Chieh, Ma, Chengqi, Wu, Bang, Zhang, Wei, Wang, Yankun, Fan, Yonglei, Poslad, Stefan, Selviah, David R., Wang, Weixi, Yuan, Hong, Yonamoto, Yoshitomo, Yamaguchi, Masahiro, Kaichi, Tomoya, Zhou, Baoding, Liu, Xu, Gu, Zhining, Yang, Chengjing, Wu, Zhiqian, Xie, Doudou, Huang, Can, Zheng, Lingxiang, Peng, Ao, Jin, Ge, Wang, Qu, Luo, Haiyong, Xiong, Hao, Bao, Linfeng, Zhang, Pushuo, Zhao, Fang, Yu, Chia-An, Hung, Chun-Hao, Antsfeld, Leonid, Chidlovskii, Boris, Jiang, Haitao, Xia, Ming, Yan, Dayu, Li, Yuhang, Dong, Yitong, Silva, Ivo, Pendão, Cristiano, Meneses, Filipe, Nicolau, Maria João, Costa, António, Moreira, Adriano, and De Cock, Cedric

Abstract

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Published
2022

12. Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Author

Potorti, Francesco, primary, Torres-Sospedra, Joaquin, additional, Quezada-Gaibor, Darwin, additional, Jimenez, Antonio Ramon, additional, Seco, Fernando, additional, Perez-Navarro, Antoni, additional, Ortiz, Miguel, additional, Zhu, Ni, additional, Renaudin, Valerie, additional, Ichikari, Ryosuke, additional, Shimomura, Ryo, additional, Ohta, Nozomu, additional, Nagae, Satsuki, additional, Kurata, Takeshi, additional, Wei, Dongyan, additional, Ji, Xinchun, additional, Zhang, Wenchao, additional, Kram, Sebastian, additional, Stahlke, Maximilian, additional, Mutschler, Christopher, additional, Crivello, Antonino, additional, Barsocchi, Paolo, additional, Girolami, Michele, additional, Palumbo, Filippo, additional, Chen, Ruizhi, additional, Wu, Yuan, additional, Li, Wei, additional, Yu, Yue, additional, Xu, Shihao, additional, Huang, Lixiong, additional, Liu, Tao, additional, Kuang, Jian, additional, Niu, Xiaoji, additional, Yoshida, Takuto, additional, Nagata, Yoshiteru, additional, Fukushima, Yuto, additional, Fukatani, Nobuya, additional, Hayashida, Nozomi, additional, Asai, Yusuke, additional, Urano, Kenta, additional, Ge, Wenfei, additional, Lee, Nien-Ting, additional, Fang, Shih-Hau, additional, Jie, You-Cheng, additional, Young, Shawn-Rong, additional, Chien, Ying-Ren, additional, Yu, Chih-Chieh, additional, Ma, Chengqi, additional, Wu, Bang, additional, Zhang, Wei, additional, Wang, Yankun, additional, Fan, Yonglei, additional, Poslad, Stefan, additional, Selviah, David R., additional, Wang, Weixi, additional, Yuan, Hong, additional, Yonamoto, Yoshitomo, additional, Yamaguchi, Masahiro, additional, Kaichi, Tomoya, additional, Zhou, Baoding, additional, Liu, Xu, additional, Gu, Zhining, additional, Yang, Chengjing, additional, Wu, Zhiqian, additional, Xie, Doudou, additional, Huang, Can, additional, Zheng, Lingxiang, additional, Peng, Ao, additional, Jin, Ge, additional, Wang, Qu, additional, Luo, Haiyong, additional, Xiong, Hao, additional, Bao, Linfeng, additional, Zhang, Pushuo, additional, Zhao, Fang, additional, Yu, Chia-An, additional, Hung, Chun-Hao, additional, Antsfeld, Leonid, additional, Chidlovskii, Boris, additional, Jiang, Haitao, additional, Xia, Ming, additional, Yan, Dayu, additional, Li, Yuhang, additional, Dong, Yitong, additional, Silva, Ivo, additional, Pendao, Cristiano, additional, Meneses, Filipe, additional, Nicolau, Maria Joao, additional, Costa, Antonio, additional, Moreira, Adriano, additional, De Cock, Cedric, additional, Plets, David, additional, Opiela, Miroslav, additional, Dzama, Jakub, additional, Zhang, Liqiang, additional, Li, Hu, additional, Chen, Boxuan, additional, Liu, Yu, additional, Yean, Seanglidet, additional, Lim, Bo Zhi, additional, Teo, Wei Jie, additional, Lee, Bu Sung, additional, and Oh, Hong Lye, additional

Published
2022
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16. Estimating TOA Reliability With Variational Autoencoders.

Author

Stahlke, Maximilian, Kram, Sebastian, Ott, Felix, Feigl, Tobias, and Mutschler, Christopher

Abstract

Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%. [ABSTRACT FROM AUTHOR]

Published
2022
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