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Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2012-08-01 , DOI: 10.1109/tim.2012.2187245
Jung Keun Lee 1 , Edward J Park , Stephen N Robinovitch
Affiliation  

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy.

中文翻译:

在各种动态条件下使用惯性传感器测量估计姿态和外部加速度

本文提出了一种基于卡尔曼滤波器的姿态(即滚转和俯仰)估计算法,该算法使用由三轴加速度计和三轴陀螺仪组成的惯性传感器。特别是,所提出的算法已被开发用于在存在外部加速度的动态条件下进行准确的姿态估计。尽管外部加速度是姿态估计误差的主要来源,并且尽管在许多应用中需要对其进行准确估计,但这个对姿态估计至关重要的问题尚未在文献中明确解决。因此,本文解决了姿态和外部加速度的组合估计问题。进行了实验测试以验证所提出算法在各种动态条件设置下的性能,并进一步了解估计精度的变化。此外,比较了在动态条件下处理估计问题的两种不同方法,即基于阈值的切换方法与基于加速度模型的方法。该算法基于外部加速度模型,能够准确估计短加速周期的姿态和外部加速度,在短期快速动态条件下显示出其高效性。相反,当测试条件涉及长时间的高外部加速度时,所提出的算法表现出逐渐增加的误差。然而,
更新日期:2012-08-01
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