Research on non-line-of-sight (NLOS) classification and error correction for ultra-wideband (UWB) Systems
The first three contributions focus on embedded on-device solutions and our contributions pave the way from 1) testing the efficacy of small and efficient machine learning models (e.g., support-vector machine (SVM)), 2) testing the consistency of different machine learning models (SVM and XGBoost trees) in different environments, and 3) enabling NLOS classification, error correction, and anchor selection on resource-constrained UWB devices using (SVM, XGBoost, and convolutional neural networks).
1) Contribution 1:
M. Stocker, M. Gallacher, C.A. Boano, and K. Römer. Performance of Support Vector Regression in Correcting UWB Ranging Measurements under LOS/NLOS Conditions. In Proceedings of the 4th International Workshop on Benchmarking Cyber-Physical Systems and Internet of Things (CPS-IoTBench), in conjunction with the CPS-IoT Week. Virtual event. May 2021. Link to paper Link to dataset
2) Contribution 2:
M. Stocker, M. Gallacher, C.A. Boano, and K. Römer. Applying NLOS Classification and Error Correction Techniques to UWB Systems: Lessons Learned and Recommendations. In Proceedings of the 6th International Workshop on Benchmarking Cyber-Physical Systems and Internet of Things (CPS-IoTBench), in conjunction with the CPS-IoT Week. San Antonio, TX, USA. May 2023. Link to paper
3) Contribution 3:
M. Gallacher, M. Stocker, M. Baddeley, K. Römer, and C.A. Boano. InSight: Enabling NLOS Classification, Error Correction, and Anchor Selection on Resource-Constrained UWB Devices. In Proceedings of the 20th International Conference on Embedded Wireless Systems and Networks (EWSN). Rende, Italy. September 2023. Link to paper Link to artefacts