[Oral Presentation]An Optimal Adaptive Kalman Filter with Gain Correction Based on Innovation Outlier Detection for Human Motion Tracking - International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence ((ICSMD2024))

An Optimal Adaptive Kalman Filter with Gain Correction Based on Innovation Outlier Detection for Human Motion Tracking

ID：72 Submission ID：119 View Protection：ATTENDEE Updated Time：2024-10-23 10:40:37 Hits：40 Oral Presentation

Start Time：2024-11-02 10:50 （Asia/Shanghai）

Duration：20min

Session：[P2] Parallel Session 2 » [P2-2] Parallel Session 2（November 2 AM）

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Abstract

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.

Keywords

Adaptive Kalman Filter, TRIAD, Kalman-gain correction, Human motion track

Speaker

WangXuhui

student School of Physics and Electronic Information,Huaibei Normal University；Anhui Province Key Laboratory of Intelligent Computing and Applications

Submission Author

WangXuhui School of Physics and Electronic Information,Huaibei Normal University；Anhui Province Key Laboratory of Intelligent Computing and Applications

ZhaoXuxing School of Physics and Electronic Information,Huaibei Normal University；Anhui Province Key Laboratory of Intelligent Computing and Applications

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Important Dates

~~15th August~~ ~~2024~~ 31st August 2024- Manuscript Submission

15th September 2024 - Acceptance Notification

1st October 2024 - Camera Ready Submission

1st October 2024 – Early Bird Registration

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