journal article·2023-09-26Open access

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

Qiuyang Dai, Faxing Lu, Junfei Xu

Applied Sciences· Volume 13· Issue 19

Abstract

The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Robotics and Sensor-Based Localization · Maritime Navigation and Safety

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China

References (36)

Show source-provided references
1. Comparison of registration error correction techniques for air surveillance radar network
2. A TSR Visual Servoing System Based on a Novel Dynamic Template Matching Method10.3390/s151229884 ↗
3. Advancing Mixture Models for Least Squares Optimization10.1109/LRA.2021.3067307 ↗
4. Wei, Z., Wei, S., Luo, F., Yang, S., and Wang, J. (2019, January 24–26). A Maximum Likelihood Registration Algorithm for Moving Dissimilar Sensors. Proceedings of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.
5. Zhao, S., Yi, M., and Liu, Z. (2022). Cooperative Anti-Deception Jamming in a Distributed Multiple-Radar System under Registration Errors. Sensors, 22.10.3390/s22197216 ↗
6. A Computationally Efficient Approach for Distributed Sensor Localization and Multitarget Tracking10.1109/LCOMM.2019.2954888 ↗
7. Single Space Based Sensor Bias Estimation Using a Single Target of Opportunity10.1109/TAES.2019.2895709 ↗
8. Performance analysis of two EM-based measurement bias estimation processes for tracking systems10.1631/FITEE.1800214 ↗
9. Li, D., Wu, D., and Lou, P. (2017, January 9–10). Exact Least Square Registration Algorithm for Multiple Dissimilar Sensors. Proceedings of the 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.10.1109/ISCID.2017.51 ↗
10. Pu, W., Liu, Y., Yan, J., Zhou, S., and Liu, H. (2017, January 5–9). A two-stage optimization approach to the asynchronous multi-sensor registration problem. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7952761 ↗
11. Approach of system error registration for two-station coast radars for sea surface monitoring10.1049/joe.2019.0749 ↗
12. Lu, X., Xie, Y., and Zhou, J. (2018). Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles. Sensors, 18.10.3390/s18061723 ↗
13. Drummond, O.E., Belfadel, D., Osborne, R.W., Bar-Shalom, Y., and Teichgraeber, R.D. (2013, January 25–29). A minimalist approach to bias estimation for passive sensor measurements with targets of opportunity. Proceedings of the Signal and Data Processing of Small Targets 2013, San Diego, CA, USA.10.1117/12.2026897 ↗
14. UAV target localization method for different field-of-view auxiliary beacons
15. Monte Carlo estimation of cooperative target-based attitude measurement system modeling and accuracy
16. Location detection and tracking of moving targets by a 2D IR-UWB radar system10.3390/s150306740 ↗
17. Julier, S.J., Uhlmann, J.K., and Durrant-Whyte, H.F. (1995, January 21–23). A new approach for filtering nonlinear systems. Proceedings of the 1995 American Control Conference-ACC′95, Seattle, WA, USA.
18. A New Adaptive Extended Kalman Filter for Cooperative Localization10.1109/TAES.2017.2756763 ↗
19. Deng, Z., Yin, L., Huo, B., and Xia, Y. (2018). Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion. Sensors, 18.10.3390/s18082406 ↗
20. Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation10.1016/j.protcy.2013.12.457 ↗
21. Cubature Kalman Filters10.1109/TAC.2009.2019800 ↗
22. Zhu, W., Wang, W., and Yuan, G. (2016). An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking. Sensors, 16.10.3390/s16060805 ↗
23. Jin, X.B., Robert Jeremiah, R.J., Su, T.L., Bai, Y.T., and Kong, J.L. (2021). The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. Sensors, 21.10.3390/s21062085 ↗
24. Gaussian mixture PHD filter for multi-sensor multi-target tracking with registration errors10.1016/j.sigpro.2012.06.030 ↗
25. Augmented state GM-PHD filter with registration errors for multi-target tracking by Doppler radars10.1016/j.sigpro.2015.09.001 ↗
26. Augmented state PHD filter for extended target tracking with bias compensation10.1016/j.ijleo.2018.01.135 ↗
27. Jain, R., Dhingra, S., Joshi, K., Rana, A.K., and Goyal, N. (2023). Enhance traffic flow prediction with Real-Time Vehicle Data Integration. J. Auton. Intell., 6.10.32629/jai.v6i2.574 ↗
28. Verma, V., Gupta, D., Gupta, S., Uppal, M., Anand, D., Ortega-Mansilla, A., Alharithi, F.S., Almotiri, J., and Goyal, N. (2022). A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle. Symmetry, 14.10.3390/sym14050960 ↗
29. Robust GPS/INS/DVL Navigation and Positioning Method Using Adaptive Federated Strong Tracking Filter Based on Weighted Least Square Principle10.1109/ACCESS.2019.2897222 ↗
30. A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment10.1007/s12145-022-00791-x ↗
31. MEKF with Navigation Frame Attitude Error Parameterization for INS/GPS10.1109/JSEN.2019.2947456 ↗
32. A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments10.1016/j.cja.2021.04.021 ↗
33. Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range–velocity measurements10.1177/1756829317745317 ↗
34. Visual Target Tracking and Relative Navigation for Unmanned Aerial Vehicles in a GPS-Denied Environment10.5139/IJASS.2014.15.3.258 ↗
35. Tang, C., Wang, Y., Zhang, L., Zhang, Y., and Song, H. (2022). Multisource Fusion UAV Cluster Cooperative Positioning Using Information Geometry. Remote Sens., 14.10.3390/rs14215491 ↗
36. Dai, Q., and Lu, F. (2023). A New Spatial Registration Algorithm of Aerial Moving Platform to Sea Target Tracking. Sensors, 23.10.2139/ssrn.4397071 ↗