[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 基于M估计的自适应鲁棒平方根连续 离散CKF算法- An Adaptive Robust Square Root Continuous Discrete CKF Algorithm Based on M Estimation
文章摘要
胡浩然, 陈树新, 吴昊, 何仁珂, 汪家宝, 郝思冲.基于M估计的自适应鲁棒平方根连续 离散CKF算法[J].空军工程大学学报:自然科学版,2022,23(1):91-96
基于M估计的自适应鲁棒平方根连续 离散CKF算法
An Adaptive Robust Square Root Continuous Discrete CKF Algorithm Based on M Estimation
  
DOI:
中文关键词: 目标跟踪  非线性滤波  连续 离散时间系统  容积卡尔曼滤波  鲁棒性
英文关键词: target tracking  nonlinear filtering  continuous discrete time system  cubature Kalman filter  robustness
基金项目:国家自然科学基金(62073337,61703420)
作者单位
胡浩然, 陈树新, 吴昊, 何仁珂, 汪家宝, 郝思冲 1.空军工程大学信息与导航学院 西安 710077 2.93184部队 北京 100076 
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中文摘要:
      针对测量中出现的异常情况,提出了一种基于M估计的自适应鲁棒平方根连续 离散容积卡尔曼滤波算法。该算法将目标跟踪问题建模为连续 离散时间模型,将改进的M估计的思想融入连续 离散容积卡尔曼滤波算法当中,通过Mahalanobis距离对异常测量进行门限判别,引入校正因子,根据观测残差自适应地调整观测噪声协方差矩阵的大小,进一步提高滤波算法的鲁棒性;通过将连续 离散模型与校正因子结合,实现了滤波精度和抗异常测量值的统一。仿真结果表明,与传统鲁棒算法相比,该算法在单点测量异常和多点测量异常的条件下都能够更加准确地对目标进行跟踪,且鲁棒性更强。
英文摘要:
      In view of the abnormal situation in the measurement, an Adaptive Robust Square root Continuous Discrete Cubature Kalman Filter algorithm is proposed based on the M estimated. The algorithm is that the target tracking problem is modeled as a continuous discrete time model, the idea of improved M estimation is integrated into the continuous discrete cubature Kalman filter algorithm, threshold abnormal measurements are made by using Mahalanobis distance, a correction factor is introduced, and the size of the observed noise covariance matrix is adaptively adjusted in accordance with the observation residuals to further improve the robustness of the filtering algorithm. And by combining the continuous discrete model with the correction factor, the filtering accuracy and the anti abnormal measurement value are unified. The simulation results show that compared with the traditional robust algorithm, MARSRCD CKF can track targets more accurately and the robustness is more strong under conditions of single point measurement abnormality and multi point measurement abnormality.
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