A Novel Recurrent Convolutional Neural Network-Based Estimation Method for Switching Guidance Law

This paper presents a recurrent convolutional neural network-based estimation method of guidance parameters of the pursuer under augmented proportional navigation (APN) guidance law with a time-varying switching navigation ratio. For the highly maneuvering pursuit-evasion process, realistic factors in the guidance law estimation are considered, such as the pursuer's estimation error and delay of the evader's acceleration in the APN guidance law. In view of the enormous measurement data, time dependency, transient change and unknown factors' disturbance in switching guidance law estimation, a novel neural network structure is built. 1-D CNN layer is used to extract features from enormous data obtained by the multiple previous measurements. The features extracted are processed by the recurrent cell to exploit the time dependency and eliminate the error caused by unknown factors. The result of ablation test shows the proposed RCNN's improved performance over single CNN or RNN. Compared to the multiple model guidance law estimation method, the proposed method can simplify the design of guidance law estimation system and reduce calculation load. The estimation result for switching guidance law shows the proposed method has higher accuracy and faster convergence rate than traditional interactive multiple model methods.