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Edge inference for UWB ranging error correction using autoencoders

Jaron Fontaine (UGent) , Matteo Ridolfi (UGent) , Ben Van Herbruggen (UGent) , Adnan Shahid (UGent) and Eli De Poorter (UGent)
(2020) IEEE ACCESS. 8. p.139143-139155
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Abstract
Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.
Keywords
Distance measurement, Error correction, Ultra wideband technology, Neural networks, Machine learning, Support vector machines, Computer, architecture, Autoencoders, edge computing, machine learning, ultra-wideband localization

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Citation

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MLA
Fontaine, Jaron, et al. “Edge Inference for UWB Ranging Error Correction Using Autoencoders.” IEEE ACCESS, vol. 8, 2020, pp. 139143–55, doi:10.1109/ACCESS.2020.3012822.
APA
Fontaine, J., Ridolfi, M., Van Herbruggen, B., Shahid, A., & De Poorter, E. (2020). Edge inference for UWB ranging error correction using autoencoders. IEEE ACCESS, 8, 139143–139155. https://doi.org/10.1109/ACCESS.2020.3012822
Chicago author-date
Fontaine, Jaron, Matteo Ridolfi, Ben Van Herbruggen, Adnan Shahid, and Eli De Poorter. 2020. “Edge Inference for UWB Ranging Error Correction Using Autoencoders.” IEEE ACCESS 8: 139143–55. https://doi.org/10.1109/ACCESS.2020.3012822.
Chicago author-date (all authors)
Fontaine, Jaron, Matteo Ridolfi, Ben Van Herbruggen, Adnan Shahid, and Eli De Poorter. 2020. “Edge Inference for UWB Ranging Error Correction Using Autoencoders.” IEEE ACCESS 8: 139143–139155. doi:10.1109/ACCESS.2020.3012822.
Vancouver
1.
Fontaine J, Ridolfi M, Van Herbruggen B, Shahid A, De Poorter E. Edge inference for UWB ranging error correction using autoencoders. IEEE ACCESS. 2020;8:139143–55.
IEEE
[1]
J. Fontaine, M. Ridolfi, B. Van Herbruggen, A. Shahid, and E. De Poorter, “Edge inference for UWB ranging error correction using autoencoders,” IEEE ACCESS, vol. 8, pp. 139143–139155, 2020.
@article{8673069,
  abstract     = {Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.},
  author       = {Fontaine, Jaron and Ridolfi, Matteo and Van Herbruggen, Ben and Shahid, Adnan and De Poorter, Eli},
  issn         = {2169-3536},
  journal      = {IEEE ACCESS},
  keywords     = {Distance measurement,Error correction,Ultra wideband technology,Neural networks,Machine learning,Support vector machines,Computer,architecture,Autoencoders,edge computing,machine learning,ultra-wideband localization},
  language     = {eng},
  pages        = {139143--139155},
  title        = {Edge inference for UWB ranging error correction using autoencoders},
  url          = {http://dx.doi.org/10.1109/ACCESS.2020.3012822},
  volume       = {8},
  year         = {2020},
}

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