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.

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Abstract: Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Abstract: Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Precision free-inertial navigation with gravity compensation by an onboard gradiometer

Abstract: Precise inertial navigation depends on external aiding to remove various systematic errors in the sensed accelerations and rotations that cause an accumulation of position error up to many hundreds of meters per hour. Technological developments in inertial sensors are underway to eliminate this dependence at the level of a few meters uncertainty over one hour of unaided inertial navigation. However, no matter how precise the inertial measurement units are, the systematic effect of unknown gravitation can cause navigation errors up to several hundred meters per hour. Compensation for this effect can take several forms, but always requires updates or information from systems external to the inertial navigator. This report analyzes the unique capabilities provided by an onboard integrated gravity gradiometer that senses the gravitational gradients, thus enabling in situ compensation. Particular attention is given to the coupling of observed gradients with angular rates and accelerations. It is shown that errors from conventional precision gyros negate the effect of gradiometer aiding on the cross-track position error if only the essential gradients are measured [accuracy of 0.1 Eotvos (E)]. With additional measurements of appropriate symmetric gradient components, both along-track and cross-track errors caused by gravitation can be controlled at the 5-m level after one hour of free-inertial navigation.

MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering

Abstract: The Attitude Heading Reference System (AHRS) has been widely used to provide the position and orientation of a rigid body. A low cost MEMS based inertial sensor measurement unit (IMU) is a core device in AHRS. To improve the AHRS system performance, there is a need to develop (i) stochastic IMU error models and (ii) random noise minimization techniques. In this paper, we modify the Sage-Husa Adaptive Kalman Filter (SHAKF) to incorporate time-varying noise estimator and robustifier, termed as Modified Sage-Husa Adaptive Robust Kalman Filter (MSHARKF). In the proposed algorithm, a three segment approach is used to evaluate the adaptive scale factor followed by the learning statistics. The scale factor is iteratively updated in the MSHARKF equations. In addition, angle random walk (ARW) and bias instability (BI) errors are represented by state-space models. The proposed algorithm is applied to restrain the drift error and random noise in the MEMS IMUs signals. The performance of this algorithm is analyzed using Allan variance (AV) analysis for static signals whereas the Root Mean Square Error (RMSE) values are evaluated for dynamic signals. Experimental results demonstrate the effectiveness of MSHARKF in reducing the drift and random noise in static and dynamic conditions as compared with other existing algorithms. Finally, we present sufficient conditions for convergence proof of the MSHARKF algorithm.

Head tracking relative to a moving vehicle or simulator platform using differential inertial sensors

Abstract: Inertial trackers have been successfully applied to a wide range of HMD applications including virtual environment training, VR gaining and even fixed-base vehicle simulation, in which they have gained widespread acceptance due to their superior resolution and low latency. Until now, it has been impossible to use inertial trackers in applications which require tracking motion relative to a moving platform, such as motion-base simulators, virtual environment trainers deployed on board ships, and live vehicular applications including helmet-mounted cueing systems and enhanced vision or situational awareness displays. This paper describes a new technique which makes it possible to use inertial head- tracking systems on-board moving platforms by computing the motion of a `tracking' Inertial Measurement Unit (IMU) mounted on the HMD relative to a `reference' IMU rigidly attached to the moving platform. Detailed kinematic equations are derived, and simulation results are provided for the particular case of an inertial tracker with drift correction by means of ultrasonic ranging sensors, but the conclusions can be applied to hybrid inertial trackers involving optical, magnetic, or RF drift correction as well.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.