DeepCIR: Insights into CIR-based Data-driven UWB Error Mitigation

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

• Vu Tran
• Zhuangzhuang Dai
• Niki Trigoni
• Andrew Markham

Keywords

• Adaptation models
• Navigation
• Neural networks
• Robot sensing systems
• Distance measurement
• Sensors
• Topology
• Error Mitigation
• High-resolution
• Synchronization
• Neural Network
• Studies In This Area
• Error Range
• Robot Navigation
• Training Set
• Convolutional Neural Network
• Time-of-flight
• Significantly Improved
• Large Errors
• Long Short-term Memory
• Distinct Locations
• Time Slot
• Indoor Environments
• Challenging Conditions
• Particle Filter
• Raspberry Pi
• Odometry
• Unseen Environments
• Seminar Room
• Vicon Motion Capture System
• Feature Extraction Block
• Rayleigh Distribution