Data Fusion for Displacement Estimation and Tracking of UAV Quadrotor in Dynamic Motion

Abstract

The fusion of MIMU and GPS data is generally used to estimate the displacement and tracking of quadrotor UAVs. Meanwhile, displacement estimation inaccuracies during dynamic motion often occur. This error is caused by noise and limited sensor sampling rate especially occurs when the quadrotor changes its attitude rapidly to generate an instantaneous horizontal force. This paper proposes data fusion based on Kalman filter to estimate orientation and displacement. Experiments were also carried out to verify displacement accuracy, i.e. in single-axis and multi-axis sensor motions. The algorithm combines data from MIMU and GPS sensors so that acceleration data is filled in points where GPS data is not available. With this method, the predicted displacement from the MIMU sensor can be corrected every second with data from the GPS and produce accurate displacement and trajectory estimates.

Keywords

Data fusion; quadrotor; navigation

Full Text:

PDF

References

X. Liu, S. Zhang, J. Tian, and L. Liu, “An onboard vision-based system for autonomous landing of a low-cost quadrotor on a novel landing pad,” Sensors (Switzerland), vol. 19, no. 21, 2019. https://dx.doi.org/10.3390/s19214703

R. Li, Q. Zhu, H. Nemati, X. Yue, and P. Narayan, “Trajectory tracking of a quadrotor using extend state observer based U-model enhanced double sliding mode control,” Journal of the Franklin Institute, vol. 360, no. 4, pp. 3520–3544, 2023. https://dx.doi.org/10.1016/j.jfranklin.2022.11.036

Y. Wang, X. Lyu, H. Gu, S. Shen, Z. Li, and F. Zhang, “Design, implementation and verification of a quadrotor tail-sitter VTOL UAV,” in 2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017, pp. 462–471, 2017. https://dx.doi.org/10.1109/ICUAS.2017.7991419

R. Ghoddousi-Fard and F. Lahaye, “High latitude ionospheric disturbances: Characterization and effects on GNSS precise point positioning,” in 2015 International Association of Institutes of Navigation World Congress (IAIN), pp. 1–6, 2015. https://dx.doi.org/10.1109/IAIN.2015.7352231

D. A. Grejner-Brzezinska, C. K. Toth, T. Moore, J. F. Raquet, M. M. Miller, and A. Kealy, “Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?,” Proceedings of the IEEE, vol. 104, pp. 1339–1353, 2016.

A. Nez, L. Fradet, F. Marin, T. Monnet, and P. Lacouture, “Identification of noise covariance matrices to improve orientation estimation by kalman filter,” Sensors (Switzerland), vol. 18, no. 10, 2018, https://dx.doi.org/10.3390/s18103490

Y. Zhuang et al., “Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches,” Information Fusion, vol. 95, pp. 62–90, 2023. https://dx.doi.org/10.1016/j.inffus.2023.01.025

S. Bijjahalli, R. Sabatini, and A. Gardi, “Advances in intelligent and autonomous navigation systems for small UAS,” Progress in Aerospace Sciences, vol. 115, p. 100617, 2020. https://dx.doi.org/10.1016/j.paerosci.2020.100617

L. Lasmadi, F. Kurniawan, and M. I. Pamungkas, “Estimasi Sudut Rotasi Benda Kaku Berbasis IMU Menggunakan Kalman Filter,” Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC), vol. 3, no. 1, pp. 57-68, 2021, https://dx.doi.org/10.28989/avitec.v3i1.909

F. Kurniawan, M. R. Erdata Nasution, O. Dinaryanto, and L. Lasmadi, “Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter,” Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC), vol. 3, no. 2, pp. 99-115, 2021. https://dx.doi.org/10.28989/avitec.v3i2.890

L. Lasmadi, F. Kurniawan, D. Dermawan, and G. N. P. Pratama, “Mobile Robot Localization via Unscented Kalman Filter,” in 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 129–132, 2019. https://dx.doi.org/10.1109/ISRITI48646.2019.9034570

R. B. Widodo and C. Wada, “Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations,” Journal of Sensor, vol. 2016, 2016. https://dx.doi.org/10.1155/2016/6943040

G. Wang et al., “A GNSS/INS Integrated Navigation Algorithm Based on Kalman Filter,” in IFAC PapersOnLine, Elsevier B.V., pp. 232–237, 2018. https://dx.doi.org/10.1016/j.ifacol.2018.08.151

G. Zhang and L.-T. Hsu, “Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system,” Aerospace Science and Technology, vol. 80, pp. 368–380, 2018. https://dx.doi.org/10.1016/j.ast.2018.07.026

Y. Guo, M. Wu, K. Tang, J. Tie, and X. Li, “Covert Spoofing Algorithm of UAV Based on GPS/INS-Integrated Navigation,” IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6557–6564, 2019. https://dx.doi.org/10.1109/TVT.2019.2914477

http://dx.doi.org/10.28989/avitec.v5i2.1758

Article Metrics

Abstract view: 179 times
Download     : 169   times

This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

• There are currently no refbacks.