Online Error Estimation of Inertial Measurement Unit in Transfer Alignment Problem

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Soltani Nejad Reza; Mohammad karimi Hamed; Naghash Abolghasem

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Journal Paper

Paper Information

Journal: AUT Journal of Modeling and Simulation Year:2024 | Volume:56 | Issue:2 Page(s): 199-208

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Information Journal Paper

Title

Online Error Estimation of Inertial Measurement Unit in Transfer Alignment Problem

Author(s)

Soltani Nejad Reza | Mohammad karimi Hamed | Naghash Abolghasem | Issue Writer Certificate

Keywords

: Inertial navigation system

In-Motion Alignment

Transfer alignment

Bias compensation

Small Rotation Angles

Abstract

The presence of bias in low-cost inertial sensors has always been a fundamental problem in the estimation of Euler angles. The Transfer alignment technique involves using two sets of sensors: a set of low-cost sensors (which may have significant bias) and a set of high-accuracy sensors (which provide reliable data). By comparing the outputs of these two sets, the biases in the low-cost sensors can be estimated and corrected. To achieve this goal, it is necessary to obtain the difference between the master and slave frames. The “master frame” typically refers to the coordinate system defined by the high-accuracy sensors, while the “slave frame” refers to the coordinate system defined by the low-cost sensors. In this article, the method of Small Rotation Angles is used to calculate this difference between the two frames. This article proposes a new alignment algorithm that is both fast and accurate. The biases of accelerometers and gyroscopes can be rapidly estimated and compensated for. The alignment discussed in this article is conducted during motion, which is more complex than stationary mode estimation. The desired maneuver in this study is a constant acceleration maneuver, where the biases of both the accelerometer and gyroscope are well estimated. For other maneuvers, gyroscope biases may not be accurately estimated, while accelerometer biases are generally well estimated. The estimated biases for accelerometers and gyroscopes are often noisy due to errors in the inertial sensors; therefore, preprocessing filtering is also performed to estimate the biases smoothly.

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