Distance and Localization Estimation on Various UWB localization targets
9Average ErrorICIR, UCIR, ACIR with BMA and BSDA
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| ICIR, UCIR, ACIR with BMA and BSDAApproach=Three CIR processing techniques (ICIR, UCIR, ACIR) with BMA and BSDA algorithms, Chip=DW1000, UWB Mobile robot=false, No Anchor Nodes=true, Angle Estimation of Objects=false, IEEE 802.15.4 Compliant=true, Target Object=whiteboard, large tv, metal box, person2025.11 | 9 | — | — | — | |
| Low-rank approximation and SVDApproach=Low-rank approximation and singular value decomposition methods, Chip=not specified, UWB Mobile robot=false, No Anchor Nodes=true, Angle Estimation of Objects=false, IEEE 802.15.4 Compliant=false, Target Object=walking person2025.11 | 20 | 50 | — | — | |
| Kalman Filter and Particle FilterApproach=Kalman Filter to reduce followed by particle filter, Chip=M-Sequence UWB, UWB Mobile robot=true, No Anchor Nodes=true, Angle Estimation of Objects=false, IEEE 802.15.4 Compliant=false, Target Object=walls and corners2025.11 | 20 | — | — | — | |
| Filtering on peak properties, SNR-score, PDoA and clusteringApproach=Filtering on peak properties, SNR-score, PDoA and clustering, Chip=QM33120WDK1, UWB Mobile robot=true, No Anchor Nodes=true, Angle Estimation of Objects=true, IEEE 802.15.4 Compliant=true, Target Object=metal plate, concrete and plywood box2025.11 | — | — | — | 11.5 | |
| UWB AoA anchor-tag extensionApproach=Extends previous approach [18] with UWB AoA anchor-tag for better loop closure, Chip=X4M300, UWB Mobile robot=true, No Anchor Nodes=false, Angle Estimation of Objects=false, IEEE 802.15.4 Compliant=false, Target Object=vertical metal rods2025.11 | — | — | 10.3 | — | |
| Wheel odometry and EKFApproach=wheel odometry and Extended Kalman Filter, Chip=X4M300, UWB Mobile robot=true, No Anchor Nodes=true, Angle Estimation of Objects=false, IEEE 802.15.4 Compliant=false, Target Object=vertical metal rods2025.11 | — | — | 6.2 | — |