### 基于改进粒子群算法辨识Volterra级数的目标机动轨迹预测

1. 空军工程大学 航空工程学院, 西安 710038
• 收稿日期:2019-05-05 修回日期:2020-05-21 发布日期:2020-07-17
• 通讯作者: 徐安 E-mail:xuankgd@163.com
• 基金资助:
空军工程大学校长基金（XZJK2019040）

### Target maneuver trajectory prediction based on Volterra series identified by improved particle swarm algorithm

XI Zhifei, XU An, KOU Yingxin, LI Zhanwu, YANG Aiwu

1. Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038
• Received:2019-05-05 Revised:2020-05-21 Published:2020-07-17
• Supported by:
Air Force Engineering University President Fund (XZJK2019040)

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.