An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking
Abstract
:1. Introduction
- A novel gyroscope drift estimation algorithm is proposed by only utilizing heading orientations and location estimations from sensor fusion system, which can be readily applied to and integrated with other systems using different sensing hardware and algorithms (further discussed in Section 2);
- The number of required UWB anchors in the fusion system can be significantly reduced by fusing only distance measurements from arbitrary number of available anchors. Conventional use of UWB in other published approaches requires that every possible location in the deployment area is covered by at least four UWB anchors. The total number of UWB anchors subsequently increases significantly for larger deployment area. When only fusing distance measurements, large area can be easily covered by significantly fewer anchors.
- Practical pedestrian tracking is designed and conducted to verify the proposed approach. The results demonstrate that the FIPS is able to achieve better tracking performance with gyroscope drift corrected.
2. Preliminaries and Related Works
3. Proposed Approach
3.1. Distance Measurements Based INS and UWB Fusion
Algorithm 1 Inertial navigation system (INS) and ultra-wideband (UWB) fusion algorithm. |
1. Initialization: 2. initialize position estimate and the particle set { = [, , ]T, (m = 1…M)} with known position Sinit = [x0, y0, zc]T 3. K UWB anchors are installed at {Ak = [, , ], k = 1…K} 4. Update: 5. at step index t 6. obtain step length Lt and orientation θt from INS subsystem 7. if no UWB measurement is available, i.e., It = 0 8. update to according to Equation (1), i.e., no additive noise 9. update particle set {, (m = 1…M)} to {, (m = 1…M)} according to Equation (1), i.e., no additive noise 10. else 11. obtain UWB distance measurements {, i = 1…It} and corresponding anchor locations {Ai, i = 1…It}, which are available for the current step (It ≤ K) 12. update particle set {, (m = 1…M)} to {, (m = 1…M)} according to Equation (4) 13. for each with anchor position Ai (i = 1…It) 14. for each particle (m = 1…M) 15. assign particle weight according to Equation (3), (5) and (6) 16. end for 17. normalize {, (m = 1…M)} such that 18. end for 19. weight of each particle 20. normalize {, (m = 1…M)} such that 21. generate position estimate 22. Resample {, (m = 1…M)}, using Systematic resampling given {, (m = 1…M)} 23. go to Update when a new step is detected at index t+1 24. end if |
3.2. Turn Detection
Algorithm 2 Turn detection algorithm |
1. Given a window of P consecutive step orientations, {Op, p = 1...P} 2. Obtain a sub-window of the first Q samples, {OFq Op, q = 1...Q} 3. Obtain a sub-window of the last Q samples, {OLq Op, q = 1...Q} 4. Calculate the heading angle range and average heading angle of {OFq, q = 1...Q} as RA1 and AA1 5. Calculate the heading angle range and average heading angle of {OLq, q = 1...Q} as RA2 and AA2 6. Calculate the heading angle difference of {AA1, AA2} as RA 7. if RA1 < THS and RA2 < THS and RA > THT 8. turn detected 9. else 10. turn not detected 11. end if |
3.3. Gyroscope Drift Estimation
3.4. Proposed FIPS with Gyroscope Drift Correction
4. Experimental Evaluations
4.1. Proposed FIPS with Gyroscope Drift Correction
4.2. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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K | P | Q | THS | THT | M | |||||
---|---|---|---|---|---|---|---|---|---|---|
~(0, 0.052) | ~(0, 0.22) | 0.1 | 0.055 | 3.5 | 2 | 15 | 6 | 5° | 55° | 2000 |
Experiment 1 | Experiment 2 | |
---|---|---|
Detected steps/actual steps | 474/502 | 453/497 |
Percentage | 94.42% | 91.15% |
Estimated distance/Actual distance (m) | 290.45/310.4 | 285.22/310.4 |
Percentage | 93.57% | 91.89% |
INS | Fusion | Fusion-Cor | |
---|---|---|---|
Experiment 1 | 2.48 m | 0.99 m | 0.88 m |
Experiment 2 | 3.27 m | 2.54 m | 2.18 m |
[16] | [18] | [26] | Proposed | |
---|---|---|---|---|
Mean position error (m) | 0.30 | 0.33 | 1.05 | 0.81/2.18 |
Total traveled distance (m) | ~30 | 100 | 1140 | 310.4 |
Number of UWB anchors | 5 | 4 | 15 | 2 |
No. of turns made | 3 | 31 | 78 | 48 |
No. of left turns | 3 | 14 | 0 | 16 |
No. of right turns | 0 | 15 | 78 | 16 |
No. of U-turns | 0 | 2 | 0 | 16 |
INS | INS | INS-Cor | |
---|---|---|---|
Experiment 1 | Mean | 3.94° | 1.75° |
SD | 2.39° | 1.29° | |
Experiment 2 | Mean | 1.56° | 1.25° |
SD | 1.00° | 0.85° |
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Tian, Q.; Wang, K.I.-K.; Salcic, Z. An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking. Sensors 2020, 20, 4476. https://doi.org/10.3390/s20164476
Tian Q, Wang KI-K, Salcic Z. An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking. Sensors. 2020; 20(16):4476. https://doi.org/10.3390/s20164476
Chicago/Turabian StyleTian, Qinglin, Kevin I-Kai Wang, and Zoran Salcic. 2020. "An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking" Sensors 20, no. 16: 4476. https://doi.org/10.3390/s20164476
APA StyleTian, Q., Wang, K. I.-K., & Salcic, Z. (2020). An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking. Sensors, 20(16), 4476. https://doi.org/10.3390/s20164476