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.

passive location  /  route planning  /  dynamic optimization  /  deep neural network  /  evolutionary computing