DeepCIR: insights into CIR-based data-driven UWB error mitigation

Conference item

Ultra-Wide-Band (UWB) ranging sensors have been widely adopted for robotic navigation thanks to their extremely high bandwidth and hence high resolution. However, off-the-shelf devices may output ranges with significant errors in cluttered, severe non-line-of-sight (NLOS) environments. Recently, neural networks have been actively studied to improve the ranging accuracy of UWB sensors using the channel-impulse-response (CIR) as input. However, previous works have not systematically evaluated the efficacy of various packet types and their possible combinations in a two-way-ranging transaction, including poll, response and final packets. In this paper, we firstly investigate the utility of different packet types and their combinations when used as input for a neural network. Secondly, we propose two novel data-driven approaches, namely FMCIR and WMCIR, that leverage two-sided CIRs for efficient UWB error mitigation. Our approaches outperform state-of-the-art by a significant margin, further reducing range errors up to 45%. Finally, we create and release a dataset of transaction-level synchronized CIRs (each sample consists of the CIR of the poll, response and final packets), which will enable further studies in this area.

Authors: Tran, V; Dai, Z; Trigoni, A; Markham, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Pages: 13300-13307
• Publication date: 2022-12-22
• Acceptance date: 2022-07-01
• Event title: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
• Event location: Kyoto, Japan
• Event website: https://iros2022.org/
• Event start date: 2022-10-23
• Event end date: 2022-10-27
• DOI: 10.1109/IROS47612.2022.9981931
• EISSN: 2153-0866
• ISSN: 2153-0858
• EISBN: 9781665479271
• ISBN: 9781665479288
• Language: English
• Keywords: FFR