Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution
Abstract
:1. Introduction
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- the position of the GNSS satellite is corrected with SBAS corrections;
- -
- the GNSS satellite clock error is corrected with SBAS corrections;
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- the ionospheric delay is determined from the SBAS model [8];
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2. Related Works
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- the use of the SBAS solution in UAV technology to perform VLOS (visual line of sight) and BVLOS (beyond visual line of sight) flights [12];
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- the determination of SBAS EGNOS (European Geostationary Navigation Overlay Service) positioning reliability for UAV technology [13];
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- the implementation of the SBAS solution in aviation operations performed in circumpolar zones with the use of UAV technology [14];
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- the application of SBAS solutions for RPAS systems (Remotely Piloted Aircraft Systems) [15];
- -
- -
- -
- the use of the SBAS solution for precise UAV positioning in real-time and post-processing mode [21];
- -
- -
- the UAV positioning using SBAS corrections as part of the PBN (performance-based navigation) navigation concept [24].
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- the reciprocal of the number of tracked satellites from a single SBAS solution;
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- the inverse of the square of the mean coordinate errors from a single SBAS solution;
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- the reciprocal of the UAV flight speed from a single SBAS solution.
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- development of a new linear combination model for the combination of a single SBAS solution from EGNOS and SDCM systems;
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- implementation of various weighing variants in order to optimize the calculation process and selection of the best method to improve the positioning accuracy of the multi-SBAS;
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- development of algorithms for assessing internal and external accuracy;
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- research on improving the determination of the vertical h component for the UAV flight.
3. Research Method
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- reciprocal of the number of tracked satellites from a single SBAS solution;
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- the inverse of the square of the mean coordinate errors from a single SBAS solution;
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- the reciprocal of the UAV flight speed from a single SBAS solution.
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- corrections along each axis of BLh components;
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- the mean error of the resultant UAV position;
- -
- the mean error of the arithmetic means for the resultant UAV position.
- (I)
- corrections along each axis of BLh components:
- —corrections along the B axis;
- —corrections along the L axis;
- —corrections along the h axis.
- (II)
- mean error of the resultant UAV position:
- —mean error for the resultant component B;
- —mean error for the resultant component L;
- —mean error for the resultant h component;
- —number of independent positioning,.
- (III)
- the mean error of the arithmetic mean for the resultant UAV position:
- —mean error of the arithmetic mean for the resultant B component;
- —mean error of the arithmetic mean for the resultant L component;
- —mean error of the arithmetic mean for the resultant h component.
4. Research Test
5. Results
6. Discussion
- -
- the improvement of the accuracy of Solution II in relation to the results from Variant I is, respectively, 2% for the B component, 1% for the L component, and 19% for the h component;
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- the improvement of the accuracy of Solution II in relation to Variant III results is 1% for the B component, 1% for the L component, and 22% for the h component.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Vehicle | Navigational Parameter | Mean Weighted Model | Assessment |
---|---|---|---|
UAV [7] | Resultant coordinates BLh of UAV [7] | Measurement weights were defined as the inverse of the squared mean error values of the determined coordinates [7] | Standard deviation of the UAV position calculated from the weighted mean model improved by about 21 ÷ 50% compared to the arithmetic mean model’s solution [7] |
Aircraft [35] | Resultant coordinates XYZ of aircraft vehicle [35] | The measurement weights are a function of the number of GPS and GLONASS satellites and the inverse of the mean error square [35] | The obtained accuracy is better by 11–87% for the model with a weighting scheme as a function of the inverse of the mean error square [35] |
Aircraft [36] | Resultant velocity of aircraft vehicle [36] | Measurement weights were defined as the inverse number of GNSS satellites [36] | The RMS error of resultant velocity is less than 0.05 m/s [36] |
Aircraft [37] | Resultant coordinates XYZ of aircraft vehicle [37] | Measurement weights were used as a function of the number of GPS satellites being tracked, and geometric PDOP (position dilution of precision) coefficient [37] | The RMS (root mean square) accuracy of positioning for XYZ geocentric coordinates was better than 1.2% to 33.7% for the weighted average method compared to a single GPS SPP solution [37] |
Accuracy Parameter | Measurement Weight | Value (m) |
---|---|---|
Between −0.757 to 0.316 | ||
Between −0.632 to 0.308 | ||
Between −0.707 to 0.296 | ||
Between −0.276 to 0.541 | ||
Between −0.291 to 0.726 | ||
Between −0.296 to 0.702 |
Accuracy Parameter | Measurement Weight | Value (m) |
---|---|---|
Between −0.417 to 0.514 | ||
Between −0.328 to 0.559 | ||
Between −0.326 to 0.509 | ||
Between −0.514 to 0.275 | ||
Between −0.469 to 0.408 | ||
Between −0.519 to 0.323 |
Accuracy Parameter | Measurement Weight | Value (m) |
---|---|---|
Between −1.244 to 1.668 | ||
Between −0.902 to 2.030 | ||
Between −0.975 to 1.652 | ||
Between −1.668 to 0.711 | ||
Between −1.305 to 1.054 | ||
Between −1.683 to 0.980 |
Coordinate | Measurement Weight | Maximum Values of | Statistical Value of |
---|---|---|---|
B | 0.416 | 3.841 | |
B | 0.885 | 3.841 | |
B | 1.487 | 3.841 | |
Coordinate | Measurement weight | Maximum values of | Statistical value of |
L | 0.242 | 3.841 | |
L | 1.064 | 3.841 | |
L | 0.509 | 3.841 | |
Coordinate | Measurement weight | Maximum values of | Statistical value of |
h | 0.787 | 3.841 | |
h | 1.711 | 3.841 | |
h | 1.199 | 3.841 |
Accuracy Parameter | Measurement Weight | Value (m) | Comment |
---|---|---|---|
0.934 | The highest accuracy is visible for the weighing Variant II | ||
0.919 | |||
0.924 | |||
0.712 | The highest accuracy is visible for the weighing Variant II | ||
0.706 | |||
0.710 | |||
0.366 | The highest accuracy is visible for the weighing Variant II | ||
0.295 | |||
0.381 |
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Krasuski, K.; Wierzbicki, D.; Bakuła, M. Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution. Remote Sens. 2021, 13, 2597. https://doi.org/10.3390/rs13132597
Krasuski K, Wierzbicki D, Bakuła M. Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution. Remote Sensing. 2021; 13(13):2597. https://doi.org/10.3390/rs13132597
Chicago/Turabian StyleKrasuski, Kamil, Damian Wierzbicki, and Mieczysław Bakuła. 2021. "Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution" Remote Sensing 13, no. 13: 2597. https://doi.org/10.3390/rs13132597
APA StyleKrasuski, K., Wierzbicki, D., & Bakuła, M. (2021). Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution. Remote Sensing, 13(13), 2597. https://doi.org/10.3390/rs13132597