High-Resolution Range Profile Target Recognition Based on GWO-RBF-BP Neural Network

High-resolution range profile (HRRP) feature plays a vital role in radar target recognition. To enhance the recognition performance, a novel GWO-RBF-BP neural network model is introduced in this paper for the recognition of four types of aerial targets. Firstly, the grey wolf optimizer (GWO) algorithm is employed to determine the optimal parameter configuration for the radial basis function (RBF) network. The well-constructed RBF network is then used as the initial parameters for further training using the backpropagation (BP) neural network, enabling the model to fully utilize both global search and local optimization capabilities. Experimental results demonstrate that the GWO-RBF-BP neural network exhibits higher recognition rate and robustness, with excellent recognition performance.