Aiming at the path planning problem of multiple unmanned aerial vehicles (UAVs) in passive localization, an unmanned aerial vehicle dynamic path planning method based on evolutionary depth neural network is proposed. Firstly, this method combines the differential evolution algorithm and BP neural network, and designs a learning path planning framework for UAV passive location based on evolutionary neural network. Then, angle of arrival (AOA) localization is used for the multiple UAVs, and an optimal training set is generated based on the Cramer-Rao low bound (CRLB) of target estimation. The optimized waypoints can be acquired from the learning behavior of the relative deployment between UAVs and target. Experimental results show that the unmanned aerial vehicle (UAV) based on the evolutionary neural network can greatly improve real-time performance and decrease location time.
YANG Junling
,
ZHOU Yu
,
WANG Weijia
,
LI Xiangyang
. Evolving deep neural network based multi-uav cooperative passive location with dynamic route planning[J]. Science & Technology Review, 2018
, 36(24)
: 26
-32
.
DOI: 10.3981/j.issn.1000-7857.2018.24.003
[1] Li X, Deng Z D, Rauchenstein L T, et al. Contributed review:Source-localization algorithms and applications using time of arrival and time difference of arrival measurements[J]. Review of Scientific Instruments, 2016, 87(4):41-50.
[2] Adelipour S, Hamdollahzadeh M, Behnia F. Constrained optimization of sensors trajectories for moving source localization using tdoa and fdoa measurements[C]//Proceedings of the 3rd RSI International Conference on Robotics and Mechatronics. Tehran, Iran:Robotics Society of Iran, 2015:200-204.
[3] Xu S, Dogancay K, Hmam H. Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking[J]. Signal Processing, 2017(133):64-78.
[4] Kruusmaa M. Global level path planning for mobile robots in dynamic environments[J]. Journal of Intelligent and Robotic Systems, 2003, 38(3):55-83.
[5] Chen C, Li H, Dong D. Hybrid control for robot navigation-a hierarchical q-learning algorithm[J]. IEEE Robotics & Automation Magazine, 2008, 15(2):37-47.
[6] Zhao Y, Li W, Shi P. A real-time collision avoidance learning system for unmanned surface vessels[J]. Neurocomputing, 2016(182):255-266.
[7] Zhang B, Mao Z, Liu W. Optimality analysis of sensor-target localization geometries[J]. Journal of Intelligent & Robotic Systems, 2015, 77(2):391-409.
[8] Xu S, Dogancay K. Optimal sensor placement for 3D angle-ofarrival target localization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3):1196-1211.
[9] Wang W, Bai P, Liang X. Performance analysis for TDOA localization using UAVs with flight disturbances[C]//Proceedings of the 20th International Conference on Information Fusion. Xi'an:IEEE, 2017.
[10] Dogancay K. UAV path planning for passive emitter localization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2):1150-1166.
[11] Cordón O. A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems:Designing interpretable genetic fuzzy systems[J]. International Journal of Approximate Reasoning, 2011, 52(6):894-913.
