Abstract
Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology.
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Angarano, S., Mazzia, V., Salvetti, F., Fantin, G., Chiaberge, M.: Robust ultra-wideband range error mitigation with deep learning at the edge. Eng. Appl. Artif. Intell. 102, 104278 (2021)
Barral, V., Escudero, C.J., García-Naya, J.A.: NLOS classification based on RSS and ranging statistics obtained from low-cost UWB devices. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5. IEEE (2019)
Bregar, K., Mohorčič, M.: Improving indoor localization using convolutional neural networks on computationally restricted devices. IEEE Access 6, 17429–17441 (2018)
Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)
Jiménez, A.R., Seco, F.: Comparing Decawave and Bespoon UWB location systems: indoor/outdoor performance analysis. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. IEEE (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
Otim, T., Díez, L.E., Bahillo, A., Lopez-Iturri, P., Falcone, F.: Effects of the body wearable sensor position on the UWB localization accuracy. Electronics 8(11), 1351 (2019)
Schmid, L., Salido-Monzú, D., Wieser, A.: Accuracy assessment and learned error mitigation of UWB ToF ranging. In: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. IEEE (2019)
Silva, B., Hancke, G.P.: IR-UWB-based non-line-of-sight identification in harsh environments: principles and challenges. IEEE Trans. Ind. Inf. 12(3), 1188–1195 (2016)
Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)
Stahlke, M., Kram, S., Mutschler, C., Mahr, T.: NLOS detection using UWB channel impulse responses and convolutional neural networks. In: 2020 International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6. IEEE (2020)
Tiwari, P., Malik, P.K.: Design of UWB antenna for the 5g mobile communication applications: a review. In: 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), pp. 24–30. IEEE (2020)
Warden, P., Situnayake, D.: TinyML. O’Reilly Media Inc. (2019)
Xiao, Z., Wen, H., Markham, A., Trigoni, N., Blunsom, P., Frolik, J.: Non-line-of-sight identification and mitigation using received signal strength. IEEE Trans. Wirel. Commun. 14(3), 1689–1702 (2014)
Ying, R., Jiang, T., Xing, Z.: Classification of transmission environment in UWB communication using a support vector machine. In: 2012 IEEE Globecom Workshops, pp. 1389–1393. IEEE (2012)
Zeng, Z., Liu, S., Wang, L.: NLOS identification for UWB based on channel impulse response. In: 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–6. IEEE (2018)
Acknowledgments
This work is partially supported by the Italian government via the NG-UWB project (MIUR PRIN 2017) and developed with the contribution of the Politecnico di Torino Interdepartmental Center for Service Robotics PIC4SeR (https://pic4ser.polito.it) and SmartData@Polito (https://smartdata.polito.it).
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Angarano, S., Salvetti, F., Mazzia, V., Fantin, G., Gandini, D., Chiaberge, M. (2022). Ultra-Low-Power Range Error Mitigation for Ultra-Wideband Precise Localization. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_56
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DOI: https://doi.org/10.1007/978-3-031-10464-0_56
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