A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging
Abstract
:1. Introduction
2. Background on UWB Based Ranging
2.1. LOS Scenario
2.2. NLOS Scenario
3. Probabilistic Ranging Error Mitigation
3.1. Building UWB Ranging Sensor Models
- TOA model , where represents the communication obstruction and material scenario, d is the true Euclidean distance between two UWB communication nodes, and denotes measured distance through TOA.
- RSS model , where s has been previously defined; represents the measured received signal strength (RSS).
3.2. Bayesian Filtering Formulation for Ranging Error Mitigation
3.2.1. Prediction
3.2.2. Update
3.3. Bayesian Filtering Formulation for Mobile Target Tracking in a Wireless Sensor Network
3.3.1. Prediction
- The target moves independently in the field, which means the current target state is only dependent on the last state, i.e., the Markov assumption. This implies that
- The transitions of obstruction scenario between the target and each reference point are independent from one another, which means , where is the transition function of that is conditional on the previous state of the target
3.3.2. Update
4. Experimental Results and Analysis
4.1. Experimental Platform
4.2. Inter-Robot Ranging Experiment Results
4.3. Mobile Robot Tracking Experiment Results
4.4. Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Abbreviations
UWB | ultra-wideband |
LOS | line-of-sight |
NLOS | non-line-of-sight |
RSS | received signal strength |
TOA | time of arrival |
GNSS | global navigation satellite system |
KF | Kalman filter |
P2P | peer-to-peer |
TDOA | time difference of arrival |
ROS | robot operating system |
RGB-D | red green blue and depth |
CTRV | constant turn rate and velocity |
RMSE | root mean squared error |
HDOP | horizontal dilution of precision |
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Subcase | Before Mitigation (mm) | TOA Model Only (mm) | Deterministic ID (mm) | Proposed Method (mm) |
---|---|---|---|---|
LOS | 16.90 | 32.63 | 15.24 | 8.19 |
15.41 | 23.57 | 12.23 | 7.42 | |
135.53 | 104.83 | 85.40 | 72.10 | |
57.33 | 57.55 | 33.00 | 28.92 |
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Xin, J.; Gao, K.; Shan, M.; Yan, B.; Liu, D. A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging. Sensors 2019, 19, 440. https://doi.org/10.3390/s19030440
Xin J, Gao K, Shan M, Yan B, Liu D. A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging. Sensors. 2019; 19(3):440. https://doi.org/10.3390/s19030440
Chicago/Turabian StyleXin, Jing, Kaiyuan Gao, Mao Shan, Bo Yan, and Ding Liu. 2019. "A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging" Sensors 19, no. 3: 440. https://doi.org/10.3390/s19030440
APA StyleXin, J., Gao, K., Shan, M., Yan, B., & Liu, D. (2019). A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging. Sensors, 19(3), 440. https://doi.org/10.3390/s19030440