Topic
Inertial reference unit
About: Inertial reference unit is a research topic. Over the lifetime, 1306 publications have been published within this topic receiving 22068 citations. The topic is also known as: IRU.
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01 Jan 2005
TL;DR: In this paper, an output model of accelerometers in inertial measurement unit (IMU) is presented and a multi-position test is adopted to calibrate the model parameters.
Abstract: Parameters calibration of output model of accelerometers in inertial measurement unit (IMU) is described. An output model is presented. Then based on this model, multi-position test is adopted to calibrate the model parameters. The calibration methods of accelerometers scale factor and the installation error coefficients are discussed. Experiment shows that these methods are effective in separating the model parameters. The approach described is of great value to engineers.
2 citations
TL;DR: In this paper , the authors proposed a dual-axis rotation scheme based on the diagonal rotation of the inertial measurement unit (IMU) body, which selects the body diagonal of the three orthogonal inertial sensors of the IMU as the horizontal rotation axis, and sets the vertical rotation axis orthogonally to this axis.
Abstract: Traditional rotational inertial navigation systems are based on rotation around one or two sensitive axes of inertial sensors. However, as the rotation and sensitive axes of inertial sensors lie along the same direction, it is difficult to modulate the relative error of the inertial sensor in the axial direction. This paper proposes a dual-axis rotation scheme based on the diagonal rotation of the inertial measurement unit (IMU) body. The scheme selects the body diagonal of the three orthogonal inertial sensors of the IMU as the horizontal rotation axis, and sets the vertical rotation axis orthogonal to this axis. As the rotation axis and the inertial sensor are oriented in different directions, at any moment of rotation, the errors of the inertial sensor in the three axial directions can all be modulated, especially the installation error. First, a mathematical model based on the diagonal rotation of the IMU body is established. On this basis, the coordinate transformation relationship and the error equations are derived, and the error propagation characteristics are obtained. Finally, the comprehensive error of the system is tested. Under the same error conditions, the system latitude error is reduced from 0.1089 nautical miles/72 h in the traditional scheme to 0.0368 nautical miles/72 h, and the longitude error is reduced from 0.3587 nautical miles/72 h to 0.1332 nautical miles/72 h. These results verify the effectiveness of the proposed scheme. This method of rotating around the body diagonal of the IMU also exhibits certain advantages when applied to other rotational inertial navigation schemes.
2 citations
28 May 2015
TL;DR: A novel way of using an Inertial Measurement Unit (IMU) to create a “Smart pen” that can detect changes in its orientation that occur during writing any word, or an alphabet.
Abstract: In this work, an innovative idea of capturing human handwriting in real time using inertial sensors is introduced. The paper presents a novel way of using an Inertial Measurement Unit (IMU) to create a “Smart pen”. This Smart Pen can detect changes in its orientation that occur during writing any word, or an alphabet. The onboard gyroscopes and accelerometers of the Inertial Measurement Unit (IMU) calculate the values of yaw, pitch and roll angles made by the 3 dimensional geometry of the pen. These values are sent to a microcontroller board using the Inter Integrated Circuit (I2C) protocol for serial communication. Depending on these values and the writing style of each letter, the Pen identifies which alphabet is written. The writing style of each person is different. Hence calibration for each person is necessary. This recognized alphabet/word can be displayed on a Liquid Crystal Display (LCD) screen which is interfaced to an Arduino board.
2 citations
25 Jul 2011
TL;DR: In this paper, an extended Kalman filter (EKF) augmented with Markov states was proposed to reduce the attitude estimation error resulting from quaternion star trackers (ST) systematic errors.
Abstract: Precision attitude determination for recent and planned space missions typically includes quaternion star trackers (ST) and a three-axis inertial reference unit (IRU). Sensor selection is based on estimates of knowledge accuracy attainable from a Kalman filter (KF), which provides the optimal solution for the case of linear dynamics with measurement and process errors characterized by random Gaussian noise with white spectrum. Non-Gaussian systematic errors in quaternion STs are often quite large and have an unpredictable time-varying nature, particularly when used in non-inertial pointing applications. Two filtering methods are proposed to reduce the attitude estimation error resulting from ST systematic errors, 1) extended Kalman filter (EKF) augmented with Markov states, 2) Unscented Kalman filter (UKF) with a periodic measurement model. Realistic assessments of the attitude estimation performance gains are demonstrated with both simulation and flight telemetry data from the Lunar Reconnaissance Orbiter.
2 citations