Accelerometer Bias Estimation for Unmanned Aerial Vehicles Using Extended Kalman Filter-Based Vision-Aided Navigation
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| Опубліковано в:: | Electronics vol. 14, no. 6 (2025), p. 1074 |
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| Автор: | |
| Інші автори: | |
| Опубліковано: |
MDPI AG
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| Онлайн доступ: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/electronics14061074 |2 doi | |
| 035 | |a 3181454507 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Belfadel, Djedjiga |u Electrical Engineering, Fairfield University, Fairfield, CT 06824, USA | |
| 245 | 1 | |a Accelerometer Bias Estimation for Unmanned Aerial Vehicles Using Extended Kalman Filter-Based Vision-Aided Navigation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate estimation of accelerometer biases in Inertial Measurement Units (IMUs) is crucial for reliable Unmanned Aerial Vehicle (UAV) navigation, particularly in GPS-denied environments. Uncompensated biases lead to an unbounded accumulation of position error and increased velocity error, resulting in significant navigation inaccuracies. This paper examines the effects of accelerometer bias on UAV navigation accuracy and introduces a vision-aided navigation system. The proposed system integrates data from an IMU, altimeter, and optical flow sensor (OFS), employing an Extended Kalman Filter (EKF) to estimate both the accelerometer biases and the UAV position and velocity. This approach reduces the accumulation of velocity and positional errors. The efficiency of this approach was validated through simulation experiments involving a UAV navigating in circular and straight-line trajectories. Simulation results show that the proposed approach significantly enhances UAV navigation performance, providing more accurate estimates of both the state and accelerometer biases while reducing error growth through the use of vision aiding from an Optical Flow Sensor. | |
| 653 | |a Velocity errors | ||
| 653 | |a Navigation systems | ||
| 653 | |a Velocity | ||
| 653 | |a Accuracy | ||
| 653 | |a Bias | ||
| 653 | |a Coordinate transformations | ||
| 653 | |a Satellite navigation systems | ||
| 653 | |a Optical flow (image analysis) | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Sensors | ||
| 653 | |a Accumulation | ||
| 653 | |a Error reduction | ||
| 653 | |a Inertial platforms | ||
| 653 | |a Accelerometers | ||
| 653 | |a Localization | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Attitudes | ||
| 653 | |a Extended Kalman filter | ||
| 653 | |a Position errors | ||
| 700 | 1 | |a Haessig, David |u AuresTech Inc., Bridgewater, NJ 08807, USA; <email>dave@aurestech.com</email> | |
| 773 | 0 | |t Electronics |g vol. 14, no. 6 (2025), p. 1074 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181454507/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181454507/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181454507/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |