Inertial Measurement Unit Rotation Research Articles

A novel error modulation rotation scheme for dual-axis rotational inertial navigation system

Abstract Dual-axis rotational inertial navigation system (RINS) can effectively suppress the error accumulation of inertial navigation system, thereby improve the long-term navigation accuracy through rotating inertial measurement unit (IMU) according to corresponding rotation scheme. The navigation accuracy of RINS is predominantly contingent on the IMU rotation scheme. A reasonable rotation scheme can effectively attenuate or even obviate the navigation errors stemming from IMU errors, whereas an ill-considered scheme may lead to swift divergence of navigation errors. In this paper, the principle of IMU errors suppression is meticulously analyzed during rotation. By comprehensively evaluating the impact of individual IMU errors on navigation accuracy alongside their corresponding error modulation characteristics, a novel 32-position rotation scheme is devised. The proposed scheme demonstrates a notable capacity to minimize the adverse effects of IMU errors on navigation accuracy and to enhance error suppression efficacy within a modulation cycle. Meanwhile, the long-endurance navigation accuracy of RINS is substantially enhanced. The efficacy of the 32-position scheme posited herein is empirically substantiated through a 24 h simulation and navigation experiment.

Design of a Multi-Position Alignment Scheme.

The current new type of inertial navigation system, including rotating inertial navigation systems and three-autonomy inertial navigation systems, has been increasingly widely applied. Benefited by the rotating mechanisms of these inertial navigation systems, alignment accuracy can be significantly enhanced by implementing IMU (Inertial Measurement Unit) rotation during the alignment process. The principle of suppressing initial alignment errors using rotational modulation technology was investigated, and the impact of various component error terms on alignment accuracy of IMU during rotation was analyzed. A corresponding error suppression scheme was designed to overcome the shortcoming of the significant scale factor error of fiber optic gyroscopes, and the research content of this paper is validated through corresponding simulations and experiments. The results indicate that the designed alignment scheme can effectively suppress the gyro scale factor error introduced by angular motion and improve alignment accuracy.

Observability Analysis and Optimization of Cooperative Navigation System With a Low-Cost Inertial Sensor Array

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in GNSS-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation modulation and cooperative navigation techniques can serve as effective ways for indoor and outdoor seamless navigation and positioning. In this paper, we propose a cooperative navigation system with a low-cost inertial sensor array composed of four IMUs. For optimizing the configuration and information processing of this system, observability analysis is carried out based on the concept of the degree of observability. Furthermore, a criterion for calculating the degree of observability is formulated to simplify the observability analysis of rotational IMUs. According to the simulation experiments of unmanned vehicle at low, medium and high driving speeds, the IMU rotation technique can improve the observability of inertial sensor errors and thus increase the accuracy of orientation, while the cooperative navigation technique can highly improve the accuracy of positioning.

Novel rotation scheme for dual-axis rotational inertial navigation system based on body diagonal rotation of inertial measurement unit

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.

Handwriting-Assistant

Pen-based handwriting has become one of the major human-computer interaction methods. Traditional approaches either require writing on the specific supporting device like the touch screen, or limit the way of using the pen to pure rotation or translation. In this paper, we propose Handwriting-Assistant, to capture the free handwriting of ordinary pens on regular planes with mm-level accuracy. By attaching the inertial measurement unit (IMU) to the pen tail, we can infer the handwriting on the notebook, blackboard or other planes. Particularly, we build a generalized writing model to correlate the rotation and translation of IMU with the tip displacement comprehensively, thereby we can infer the tip trace accurately. Further, to display the effective handwriting during the continuous writing process, we leverage the principal component analysis (PCA) based method to detect the candidate writing plane, and then exploit the distance variation of each segment relative to the plane to distinguish on-plane strokes. Moreover, our solution can apply to other rigid bodies, enabling smart devices embedded with IMUs to act as handwriting tools. Experiment results show that our approach can capture the handwriting with high accuracy, e.g., the average tracking error is 1.84mm for letters with the size of about 2cmx1cm, and the average character recognition rate of recovered single letters achieves 98.2% accuracy of the ground-truth recorded by touch screen.

Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

Comprehensive Error Compensation for Dual-Axis Rotational Inertial Navigation System

For rotational inertial navigation system(RINS), the system error can be compensated by rotating the inertial measurement unit (IMU) with one or more axis regularly, and the error compensation effect is determined by the rotation scheme of IMU.A reasonable rotation scheme should remove the system errors caused by inertial sensors errors and meanwhile, should not introduce other additional errors by the rotation. The error compensation demerits of two common rotation schemes are discussed. In these two schemes, the attitude errors caused by installation error are not modulated completely in a whole rotation period, which will cause accumulative velocity errors by integration. The accumulative velocity errors will sequentially increase the amplitude of position oscillation error. Based on the aforementioned analysis, a comprehensive error compensation scheme for dual-axis RINS is proposed. The proposed scheme can not only modulate the constant bias of inertial sensors, but also compensate the scale factor errors and installation errors coupled with IMU rotation. Moreover, the coupling errors are modulated into a zero-mean form with half amplitude of those two traditional schemes. As a result, the system errors of RINS with this rotation scheme can be modulated to a maximum extent. The superiority of the proposed scheme is verified through simulations with 4 different error conditions. Under the same synthesized error condition, the position error of the proposed scheme decreases to 0.37nmile/72hours from 0.98nmile/72hours in the other two schemes.

Improving Observability of an Inertial System by Rotary Motions of an IMU.

It has been identified that the inertial system is not a completely observable system in the absence of maneuvers. Although the velocity errors and the accelerometer bias in the vertical direction can be solely observable, other error states, including the attitude errors, the accelerometer biases in the east and north directions, and the gyro biases, are just jointly observable states with velocity measurements, which degrades the estimation accuracy of these error states. This paper proposes an innovative method to improve the system observability for a Micro-Electro-Mechanical-System (MEMS)-based Inertial Navigation System (INS) in the absence of maneuvers by rotary motions of the Inertial Measurement Unit (IMU). Three IMU rotation schemes, namely IMU continuous rotation about the X, Y and Z axes are employed. The observability is analyzed for the rotating system with a control-theoretic approach, and tests are also conducted based on a turntable to verify the improvements on the system observability by IMU rotations. Both theoretical analysis and the results indicate that the system observability is improved by proposed IMU rotations, the roll and pitch errors, the accelerometer biases in the east and north directions, the gyro biases become observable states in the absence of vehicle maneuvers. Although the azimuth error is still unobservable, the enhanced estimability of the gyro bias in the vertical direction can effectively mitigate the azimuth error accumulation.

A compensation method of lever arm effect for tri-axis hybrid inertial navigation system based on fiber optic gyro

Hybrid inertial navigation system (HINS) is a new kind of inertial navigation system (INS), which combines advantages of platform INS, strap-down INS and rotational INS. HINS has a physical platform to isolate the angular motion as platform INS does, HINS also uses strap-down attitude algorithms and applies rotation modulation technique. Tri-axis HINS has three gimbals to isolate the angular motion in the dynamic base, in which way the system can reduce the effects of angular motion and improve the positioning precision. However, the angular motion will affect the compensation of some error parameters, especially for the lever arm effect. The lever arm effect caused by position errors between the accelerometers and rotation center cannot be ignored due to the rapid rotation of inertial measurement unit (IMU) and it will cause fluctuation and stage in velocity in HINS. The influences of angular motion on the lever arm effect compensation are analyzed firstly in this paper, and then the compensation method of lever arm effect based on the photoelectric encoders in dynamic base is proposed. Results of experiments on turntable show that after compensation, the fluctuations and stages in velocity curve disappear.

MEMS IMU Error Mitigation Using Rotation Modulation Technique.

Micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) outputs are corrupted by significant sensor errors. The navigation errors of a MEMS-based inertial navigation system will therefore accumulate very quickly over time. This requires aiding from other sensors such as Global Navigation Satellite Systems (GNSS). However, it will still remain a significant challenge in the presence of GNSS outages, which are typically in urban canopies. This paper proposed a rotary inertial navigation system (INS) to mitigate navigation errors caused by MEMS inertial sensor errors when external aiding information is not available. A rotary INS is an inertial navigator in which the IMU is installed on a rotation platform. Application of proper rotation schemes can effectively cancel and reduce sensor errors. A rotary INS has the potential to significantly increase the time period that INS can bridge GNSS outages and make MEMS IMU possible to maintain longer autonomous navigation performance when there is no external aiding. In this research, several IMU rotation schemes (rotation about X-, Y- and Z-axes) are analyzed to mitigate the navigation errors caused by MEMS IMU sensor errors. As the IMU rotation induces additional sensor errors, a calibration process is proposed to remove the induced errors. Tests are further conducted with two MEMS IMUs installed on a tri-axial rotation table to verify the error mitigation by IMU rotations.

A Motion Tracking and Sensor Fusion Module for Medical Simulation.

Here we introduce a motion tracking or navigation module for medical simulation systems. Our main contribution is a sensor fusion method for proximity or distance sensors integrated with inertial measurement unit (IMU). Since IMU rotation tracking has been widely studied, we focus on the position or trajectory tracking of the instrument moving freely within a given boundary. In our experiments, we have found that this module reliably tracks instrument motion.

On the effectiveness of rotation of the inertial measurement unit of a FOG-based platformless ins for marine applications

The errors of an autonomous platformless inertial navigation system (INS) with fiber-optic gyros (FOG), the inertial measurement unit (IMU) of which is mounted in a two-axis gimbal suspension, are analyzed. The quality criteria and the kinematic scheme of the gimbal are considered. The optimal law of IMU rotation from the standpoint of the selected minimum criterion has been calculated. Simulation was carried out to study the effects of different errors of the IMU sensors on the INS accuracy. The results obtained are confirmed by the development tests of a FOG-based INS designed by Concern CSRI Elektropribor.

Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.