报告详情
An Optimal Adaptive Kalman Filter with Gain Correction Based on Innovation Outlier Detection for Human Motion Tracking
编号：72
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更新：2024-10-23 10:40:37
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口头报告
报告开始：2024年11月02日 10:50（Asia/Shanghai）
报告时间：20min
所在会场：[P2] Parallel Session 2 [P2-2] Parallel Session 2（November 2 AM）
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摘要
In this article, an optimal adaptive Kalman filter algorithm is proposed using an inertial measurement unit (IMU) equipped with a three-axis gyroscope, accelerometer, and magnetometer for human motion tracking. The proposed algorithm introduces an adaptive factor to adjust the process noise covariance matrix, effectively compensating for measurement noise and modeling errors. To suppress the impact of outliers, three-segmented weight functions are constructed to apply appropriate weights to the innovation. When the weight function for suppressing outliers enters the rejection domain, calibrating the Kalman gain is used to correct the posterior covariance and suppress abnormal values. This approach effectively prevents filter divergence that can occur when the continuous innovation sequence is set to zero without adjusting the posterior covariance. The estimated gravity acceleration and geomagnetic field are then used to calculate the Euler angles via a triaxial attitude determination algorithm. Finally, The effectiveness of the proposed method is demonstrated through various action experiments, with experimental results showing a significant reduction in errors.
关键词
Adaptive Kalman Filter, TRIAD, Kalman-gain correction, Human motion track
报告人
WangXuhui
student School of Physics and Electronic Information,Huaibei Normal University；Anhui Province Key Laboratory of Intelligent Computing and Applications稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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