首页 >

基于交互多模型的时变平滑变结构滤波算法

王健,周立辉,陈家福,李欣琦,郭霖佯,何自豪,周浩   

  1. 西北工业大学
  • 收稿日期:2024-01-17 修回日期:2024-04-18 出版日期:2024-04-25 发布日期:2024-04-25
  • 通讯作者: 王健
  • 基金资助:
    国家自然科学基金;国家自然科学基金;陕西省重点产业创新链项目;陕西省重点产业创新链项目;陕西省重点产业创新链项目

Time-varying smooth variable structure filter based on interactive multi-model

  • Received:2024-01-17 Revised:2024-04-18 Online:2024-04-25 Published:2024-04-25

摘要: 针对平滑变结构滤波算法存在抖振以及无法有效估计未量测目标状态的问题,提出了基于交互多模型的时变平滑变结构滤波算法。该算法首先通过平滑变结构滤波算法对目标状态进行初步估计;其次通过计算时变平滑有界层,并采用tanh函数取代饱和函数计算初步状态增益,解决抖振问题;然后采用贝叶斯思想重新计算协方差矩阵与状态增益用于目标状态更新,解决平滑变结构滤波无法有效估计未量测状态的问题;最后与交互多模型算法结合,实现对机动目标的有效跟踪。仿真结果表明,本文提出的算法在模型失配以及量测噪声改变的情况下,仍可有效的对机动目标进行跟踪,仿真结果与典型的目标跟踪方法相比,跟踪精度明显提高且鲁棒性更强。

关键词: 平滑变结构滤波, 时变平滑有界层, 交互多模型, 状态估计, 机动目标跟踪

Abstract: Aiming at the problems of jitter and ineffective estimation of the unmeasured target state in the smooth variable structure filter, a time-varying smooth variable structure filter based on interactive multi-model is proposed. The algorithm initiates by estimating the target state through the smooth variable structure filter. Subsequently, it resolves the jitter problem by computing the time-varying smooth boundary layer and employing the tanh function instead of the saturation function to calculate the initial state gain. Following this, Bayesian formulas are applied to recompute the covariance matrix and state gain for updating the target state, effectively addressing the challenge of inefficiently estimating unmeasured states in the smooth variable structure filter. Lastly, the algorithm is integrated with the interactive multi-model approach to achieve effective tracking of maneuvering targets. Simulation results demonstrate that the proposed algorithm maintains its effectiveness in tracking maneuvering targets under conditions of model mismatch and changes in measurement noise. The simulation results indicate a significant improvement in tracking accuracy and enhanced robustness compared to typical target tracking methods.

Key words: smooth variable structure filter, time-varying smoothing boundary layer, interactive multi-model, state estimation, maneuvering target tracking

中图分类号: