Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

Abstract: Inertial measurement units (IMUs) are widely used in motion measurement. However, the drift of IMUs results in significant accumulated errors for long-term position and orientation measurement. This paper reports a method to compensate the drift of inertial sensors with the assist of ultrasonic sensors and magnetometers. The magnetometer is combined with a three-axis accelerometer as a digital compass for orientation determination in static state by measuring the gravity and the earth's magnetic field vectors. A three-axis gyroscope is used to measure the orientation in dynamic state to complement the digital compass. Displacement determination is implemented using the accelerometer through double integration, and an ultrasonic sensor is employed to periodically calibrate the accumulated errors of the accelerometer. The redundant data from the multi-sensors are fused using extended Kalman filter (EKF), in which the position measured by the ultrasonic sensor and the orientation measured by the digital compass are defined as the observation values, and the position, velocity, and orientation are included in the state vector. Experimental results show that the accumulated errors of inertial sensors are reduced by ultrasonic sensors and magnetometers, and EKF improves the accuracy of orientation and position measurements.

Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application

Abstract: ............................................................................................................................................... III ACKNOWLEDGEMENTS.........................................................................................................................V DEDICATION............................................................................................................................................ VI TABLE OF CONTENTS..........................................................................................................................VII LIST OF TABLES ..................................................................................................................................... XI LIST OF FIGURES ................................................................................................................................ XIII LIST OF SYMBOLS............................................................................................................................... XVI LIST OF ABBREVIATIONS................................................................................................................. XIX CHAPTER ONE : INTRODUCTION.........................................................................................................1 1.1 BACKGROUND .......................................................................................................................................1 1.2 LOW COST INERTIAL SENSORS ..............................................................................................................5 1.2.1 MEMS Inertial Sensors .................................................................................................................5 1.2.2 Performance Characteristics ........................................................................................................6 1.3 PREVIOUS RESEARCH AND THEIR LIMITATIONS.....................................................................................8 1.3.1 Integration Strategy ......................................................................................................................8 1.3.2 Initial Alignment .........................................................................................................................10 1.3.3 Sensor Error Calibration/Modeling............................................................................................11 1.3.4 Rapid Degradation in Solution during GPS Outage...................................................................13 1.3.5 Operational Environment ...........................................................................................................16 1.4 OBJECTIVES.........................................................................................................................................17 1.5 RESEARCH METHODOLOGY SUMMARY ...............................................................................................19

Indoor Positioning Using Ultrawideband and Inertial Measurements

Abstract: In this paper, we present an approach to combine measurements from inertial sensors (accelerometers and gyroscopes) with time-of-arrival measurements from an ultrawideband (UWB) system for indoor positioning. Our algorithm uses a tightly coupled sensor fusion approach, where we formulate the problem as a maximum a posteriori (MAP) problem that is solved using an optimization approach. It is shown to lead to accurate 6-D position and orientation estimates when compared to reference data from an independent optical tracking system. To be able to obtain position information from the UWB measurements, it is imperative that accurate estimates of the UWB receivers' positions and their clock offsets are available. Hence, we also present an easy-to-use algorithm to calibrate the UWB system using a maximum-likelihood (ML) formulation. Throughout this work, the UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution to account for measurement outliers. The heavy-tailed asymmetric distribution works well on experimental data, as shown by analyzing the position estimates obtained using the UWB measurements via a novel multilateration approach.

Navigation with IMU/GPS/digital compass with unscented Kalman filter

Abstract: Autonomous vehicle navigation with standard IMU and differential GPS has been widely used for aviation and military applications. Our research interesting is focused on using some low-cost off-the-shelf sensors, such as strap-down IMU, inexpensive single GPS receiver. In this paper, we present an autonomous vehicle navigation method by integrating the measurements of IMU, GPS, and digital compass. Two steps are adopted to overcome the low precision of the sensors. The first is to establish sophisticated dynamics models which consider Earth self rotation, measurement bias, and system noise. The second is to use a sigma Kalman filter for the system state estimation, which has higher accuracy compared with the extended Kalman filter. The method was evaluated by experimenting on a land vehicle equipped with IMU, GPS, and digital compass.

Integrated-strapdown-air-data sensor system

Abstract: A plurality of inertial measuring unit (IMU) modules (41A, B, C and D) each comprising gyros and accelerometers (61, 65 and 67) for sensing inertial information along two orthogonal axes, are strapdown mounted in an aircraft, preferably such that the sense axes of the IMUs are skewed with respect to one another. Inertial and temperature signals produced by the IMU modules, plus pressure signals produced by a plurality of pressure transducer modules (43A, B and C) and air temperature signals produced by total air temperature sensors (45A and B) are applied to redundant signal processors (47A, B and C). The signal processors convert the raw analog information signals into digital form, error compensate the incoming raw digital data and, then, manipulate the compensated digital data to produce signals suitable for use by the automatic flight control, pilot display and navigation systems of the aircraft. The signal processors include: an interface system comprising a gyro subsystem (47), an accelerometer and air calibration data subsystem (50) and an air data and temperature subsystem (52); a computer (54); an instruction decoder ( 56); and, a clock (58). During computer interrupt intervals raw digital data is fed to the computer (54) by the interface subsystems under the control of the instruction decoder (56). The computer includes a central processing unit that compensates raw digital gyro and accelerometer data to eliminate bias, scale factor, dynamic and temperature errors, as necessary. The central processing unit also modifies the gyro and accelerometer data to compensate for relative misalignment between the sense axes of the gyros and accelerometers and for the skewed orientation of these sense axes relative to the yaw, roll and pitch axes of the aircraft. Further, accelerometer data is transformed from body coordinate form to navigational coordinate form and the result used to determine the velocity and position of the aircraft. Finally, the central processing unit develops initializing alignment signals and develops altitude, speed and corrected temperature and pressure signals.