Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Abstract

Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function (PSR-RBF) neural network is established by combining the characteristics of trajectory with time continuity. In order to further improve the prediction performance of the model, the rival penalized competitive learning (RPCL) algorithm is introduced to determine the structure of RBF, the Levenberg-Marquardt (LM) and the hybrid algorithm of the improved particle swarm optimization (IPSO) algorithm and the k-means are introduced to optimize the parameter of RBF, and a PSR-RBF neural network is constructed. An independent method of 3D coordinates of the target maneuver trajectory is proposed, and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument (ACMI), and the maneuver trajectory prediction model based on the PSR-RBF neural network is established. In order to verify the precision and real-time performance of the trajectory prediction model, the simulation experiment of target maneuver trajectory is performed. The results show that the prediction performance of the independent method is better, and the accuracy of the PSR-RBF prediction model proposed is better. The prediction confirms the effectiveness and applicability of the proposed method and model.