基于鲁棒容积卡尔曼滤波的自适应目标跟踪算法
Adaptive target tracking algorithm based on robust cubature Kalman filter
摘要点击 53  全文点击 63  投稿时间:2019-03-21  修订日期:2019-07-27
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DOI编号  10.7641/CTA.2019.90159
  2020,37(4):793-800
中文关键词  野值  非线性估计  自适应  修正因子  目标跟踪
英文关键词  outliers  nonlinear estimation  adaptive  correction factor  target tracking
基金项目  国家自然科学基金
学科分类代码  
作者单位E-mail
彭美康 武汉理工大学高性能舰船技术教育部重点实验室 929602821@whut.edu.cn 
郭蕴华 武汉理工大学高性能舰船技术教育部重点实验室 wtugyh@163.com 
汪敬东 武汉理工大学高性能舰船技术教育部重点实验室  
牟军敏 武汉理工大学高性能舰船技术教育部重点实验室  
胡义 武汉理工大学高性能舰船技术教育部重点实验室  
中文摘要
      观测野值将大大降低滤波算法的估计精度.为了解决这个问题,提出了一种基于鲁棒M估计的自适应CKF算法.借鉴Huber等价权函数的思想,构造了基于平方根平滑逼近函数的修正因子以抑制观测野值的影响,并结合Cubature卡尔曼滤波器求解框架推导出该算法.理论分析证明该算法具有较好的数值稳定性.仿真实验表明,该算法能够自适应地减少异常值的不利影响,并且与现有算法相比具有更优的滤波性能且不会大幅增加计算成本.
英文摘要
      The outliers in observations will greatly reduce the estimation accuracy of the filtering algorithm. In order to address this problem, an adaptive CKF algorithm based on robust M-estimation is proposed. Inspired by the idea of the Huber equivalent weight function, a correction factor based on the square root smooth approximation function is constructed to suppress the influence of outliers, and the proposed algorithm is derived combined with the cubature Kalman filter solution framework. Theoretical analysis proves that the algorithm has better numerical stability. Simulation experiments show that the proposed algorithm can adaptively reduce the adverse effects of the outlier and exhibit superior filter performance compared to the existing algorithms without greatly increasing the computational cost.