Time-Constrained Cooperative Guidance Based on Deep Neural Network

Time-constrained cooperative guidance based on deep neural network (DNN) is developed to address the problem of cooperative guidance of multiple missiles against maneuvering targets. Firstly, deep neural network is applied to estimate time-to-go. A simplified dataset is created to improve the estimation effect and the performance of training results is good. Secondly, time-constrained cooperative guidance law is designed. Time-constrained guidance (TCG) law based on variable navigation ratio is proposed and an optimal method of cooperative time is designed. Thirdly, simulation in 3-D space and equivalent experiment in 2-D space are carried out to verify the performance of guidance law. An equivalent experiment platform based on multiple unmanned ground vehicles (Multi-UGVs) is erected and the experiment to achieve cooperative guidance is carried out.