Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm

XU Shu-sheng; LIN Xiao-gong; LI Xin-fei

doi:10.3969/j.issn.0372-2112.2014.12.009

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Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm

更新时间：2025-07-16

- Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm
- Acta Electronica Sinica Vol. 42, Issue 12, Pages: 2394-2400(2014)
- 作者机构：
  
  1. 青岛农业大学机电工程学院,山东,青岛,266109
  2. 哈尔滨工程大学自动化学院,黑龙江,哈尔滨,150001
  3. 青岛农业大学机电工程学院,山东,青岛,266109
  4. 哈尔滨工程大学自动化学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61203255, No.51209051)
- DOI：10.3969/j.issn.0372-2112.2014.12.009
  CLC： TP273
- Published：2014
- 稿件说明：
移动端阅览
XU Shu-sheng, LIN Xiao-gong, LI Xin-fei. Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm[J]. Acta Electronica Sinica, 2014, 42(12): 2394-2400.
DOI：

XU Shu-sheng, LIN Xiao-gong, LI Xin-fei. Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm[J]. Acta Electronica Sinica, 2014, 42(12): 2394-2400. DOI： 10.3969/j.issn.0372-2112.2014.12.009.

摘要

针对强跟踪滤波器(STF)的理论局限性及不良测量导致的滤波性能下降问题

提出了一种强跟踪自适应平方根容积卡尔曼滤波(SRCKF)算法.利用新息协方差匹配原理

建立对不良测量具有鲁棒性的自适应SRCKF.基于STF的理论框架

采用自适应SRCKF代替扩展卡尔曼滤波构建强跟踪自适应SRCKF.该算法兼具STF与自适应SRCKF的优点

在系统同时存在模型不确定性及不良测量时具有良好的滤波性能.仿真验证了所建算法的有效性.

Abstract

To overcome problems that malfunctions in the measurement system lead to degradation of performance of strong tracking filter (STF) and inherent disadvantages of STF

an adaptive square-root cubature Kalman filter (SRCKF) algorithm is proposed.With innovation covariance matching techniques an adaptive SRCKF is built

which is insensitive to measurement malfunctions.Strong tracking adaptive SRCKF views STF as the basic theory framework and makes adaptive SRCKF to replace extended Kalman filter (EKF)

so it has the advantages of STF and adaptive SRCKF.In case of model uncertainty of system and measurement malfunctions

the proposed algorithm has strong robustness and high accuracy.Simulation results show the effectiveness of the presented algorithm.

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