关键词: 混沌理论, 轨迹预测, Volterra泛函模型, 改进粒子群算法, 参数辨识

Abstract: Target maneuver trajectory prediction plays an important role in air combat situation awareness and target threat assessment. Aiming at the problems of high complexity and low prediction accuracy in the traditional method, this paper proposes a target maneuvering trajectory prediction model based on the phase space reconstruction theory and Volterra functional series, combining the chaotic characteristics of the target maneuvering trajectory time series. The 0-1 test method is firstly used to verify the chaotic characteristics of the target maneuvering trajectory time series, followed by determination of the embedding dimension and time delay by the C-C method. The target maneuvering trajectory time series is further reconstructed. The Volterra functional series prediction model is introduced. However, the identification of higher-order Volterra kernel function is difficult. To solve this problem, we propose a Modified Particle Swarm Optimization algorithm (MPSO) combining the chaotic strategy and adaptive strategy, construct a Volterra series prediction model identified by the MPSO, and apply the model to target maneuvering trajectory prediction. Finally, the algorithm proposed in this paper is compared with the Kalman filter and machine learning algorithm for single-step and multi-step prediction. Meanwhile, the performance of MPSO is compared with that of other intelligent algorithms. The simulation results show good performance of the proposed prediction model in both single-step and multi-step prediction, and fast, accurate identification of the Volterra series model parameters by the MPSO.

Key words: chaos theory, trajectory prediction, Volterra functional model, improved particle swarm algorithm, parameter identification