Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking
Source abstract
Abstract
In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Distributed Control Multi-Agent Systems · UAV Applications and Optimization · Target Tracking and Data Fusion in Sensor Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
References (26)
Show source-provided references
1. Fu, X.W., Liu, K.P., and Gao, X.G. (2018). Multi-UAVs communication-aware cooperative target tracking. Appl. Sci., 8.10.3390/app8060870 ↗
2. Distributed estimation for moving target based on state-consensus strategy10.1109/TAC.2013.2246476 ↗
3. Fu, X.W., Bi, H.Y., and Gao, X.G. (2017). Multi-UAVs cooperative localization algorithms with communication constraints. Math. Probl. Eng., 1943539.10.1155/2017/1943539 ↗
4. Robust information filter based on maximum correntropy criterion10.2514/1.G001576 ↗
5. UAV Mobile ground target pursuit algorithm10.1007/s10846-012-9690-9 ↗
6. Event-triggered cooperative target tracking in wireless sensor networks10.1016/j.cja.2016.08.010 ↗
7. Ridley, M., Nettleton, E., Göktogan, A., Brooker, G., Sukkarieh, S., and Durrant-Whyte, H.F. (2003). Decentralized ground target tracking with heterogeneous sensing nodes on multiple UAVs. Information Processing in Sensor Networks, Springer.10.1007/3-540-36978-3_37 ↗
8. Casbeer, D.W., and Beard, R. (2009, January 10–12). Distributed information filtering using consensus filters. Proceedings of the American Control Conference, St. Louis, MO, USA.10.1109/ACC.2009.5160531 ↗
9. Wang, L., Li, Y., Zhu, H., and Shen, L. (2010, January 9–11). Target state estimation and prediction based standoff tracking of ground moving target using a fixed-wing UAV. Proceedings of the 8th IEEE International Conference on Control and Automation (ICCA), Xiamen, China.10.1109/ICCA.2010.5524326 ↗
10. Chung, T.H., Burdick, J.W., and Murray, R.M. (2006, January 15–19). A decentralized motion coordination strategy for dynamic target tracking. Proceedings of the IEEE Conference on Robotics and Automation, Orlando, FL, USA.
11. Yang, P., Freeman, R.A., and Lynch, K.M. (2007, January 10–14). Distributed cooperative active sensing using consensus filters. Proceedings of the IEEE Conference on Robotics and Automation, Roma, Italy.10.1109/ROBOT.2007.363820 ↗
12. Coordinated standoff tracking of moving targets using Lyapunov guidance vector fields10.2514/1.30507 ↗
13. Coordinated standoff tracking of moving target groups using multiple UAVs10.1109/TAES.2015.140044 ↗
14. Ponda, S.S., Kolacinski, R.M., and Frazzoli, E. (2009, January 10–13). Trajectory optimization for target localization using small unmanned aerial vehicles. Proceedings of the AIAA Guidance, Navigation and Control, Chicago, IL, USA.10.2514/6.2009-6015 ↗
15. Wang, L., Su, F., Zhu, H., and Shen, L. (2010, January 27–29). Active sensing based cooperative target tracking using UAVs in an urban area. Proceedings of the IEEE International Conference on Advanced Computer Control, Shenyang, China.
16. An overview of recent progress in the study of distributed multi-agent coordination10.1109/TII.2012.2219061 ↗
17. Calvo, J.A.L., Alirezaei, G., and Mathar, R. (2017, January 10–12). Wireless powering of drone-based MANETs for disaster zones. Proceedings of the IEEE International Conference on Wireless for Space and Extreme Environments, Montreal, QC, Canada.10.1109/WiSEE.2017.8124900 ↗
18. Cooperative target localization with a communication-aware unmanned aircraft system10.2514/1.51591 ↗
19. Olfati-Saber, R., and Murray, R.M. (2002, January 10–13). Graph rigidity and distributed formation stabilization of multi-vehicle systems. Proceedings of the IEEE Conference on the Decision and Control, Las Vegas, NV, USA.
20. Vector Field Path Following for Miniature Air Vehicles10.1109/TRO.2007.898976 ↗
21. The GRASP Multiple Micro-UAV Testbed10.1109/MRA.2010.937855 ↗
22. Performance and Lyapunov stability of a nonlinear path-following guidance method10.2514/1.28957 ↗
23. Effective real-time unmanned air vehicle path planning in presence of threat netting
24. Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles10.1109/TVT.2009.2015953 ↗
25. Liu, Z., Gao, X.G., and Fu, X.W. (2018). A cooperative search and coverage algorithm with controllable revisit and connectivity maintenance for multiple unmanned aerial vehicles. Sensors, 18.10.3390/s18051472 ↗
26. Distributed minimum spanning tree maintenance for transient node failures10.1109/TC.2010.228 ↗