Abstract
This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).
Highlights
Applications of inertial measurement units (IMUs) consisting of accelerometers and gyroscopes have been exponentially increasing as these units can function as wearable motion sensors because of their sourceless property, i.e., they do not require external location-fixed sources [1,2,3,4,5,6]
The attitude estimation Kalman filter (KF) proposed in [14] predicts the attitude in the time update using the angular velocity measured from the gyroscope and performs the measurement update using the gravitational acceleration measured from the accelerometer
Case 1 investigates the effect of the integration order on the estimation accuracy when both the transient matrix shown in (15) and the process noise covariance matrix shown in (16) are varied according to the selected order
Summary
Applications of inertial measurement units (IMUs) consisting of accelerometers and gyroscopes have been exponentially increasing as these units can function as wearable motion sensors because of their sourceless property, i.e., they do not require external location-fixed sources [1,2,3,4,5,6]. In spite of the varieties of the algorithms, the basic concept of estimation is common; that is, gyroscope signals are integrated to predict the attitude, and accelerometer signals are used to prevent the drift error caused by the error accumulation associated with the integration. The first prediction step is called strapdown integration as the sensors are rigidly strapped to a body that we want to track. A higher-order scheme may be more appropriate, as discussed in [17]. Once the integration order is chosen, the remainder of the Taylor series expansion
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