MEMS-based rotary strapdown inertial navigation system

Sun, Wei; Wang, Daxue; Xu, Lianfei; Xu, Lingling

doi:10.1016/j.measurement.2013.04.035

Cited by 63 publications

(48 citation statements)

References 3 publications

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“…Similarly, the IMU rotation rate will be projected to the Y- and Z-axes due to gyro installation errors, and leads to additional gyro biases as shown in Equation (16). Although the rotation-induced gyro biases can be modulated, and the resulted attitude errors are removed after a complete rotation cycle as shown in Equation (17), it still degrades the navigation solutions within the rotation cycle [17,26].

δ ω_{S F}^{n} = C_{s}^{n} S_{g} ω_{i s}^{s} = [\begin{matrix} K_{g x} ω \\ K_{g y} false(ω_{i e} \cos φ \frac{1 + \cos 2 ω t}{2} + ω_{i e} \sin φ \frac{\sin 2 ω t}{2} false) \\ - K_{g z} false(- ω_{i e} \cos φ \frac{1 - \cos 2 ω t}{2} + ω_{i e} \sin φ \frac{\sin 2 ω t}{2} false) \\ K_{g y} false(ω_{i e} \cos φ \frac{\sin 2 ω t}{2} + ω_{i e} \sin φ \frac{1 - \cos 2 ω t}{2} false) \\ + K_{g z} false(- ω_{i e} \cos φ \frac{\sin 2 ω t}{2} + ω_{i e} \sin φ \frac{1 + \cos 2 ω t}{2} false) \end{matrix}]

true \int_{0}^{T} δ ω_{S F}^{n} d t = [\begin{matrix} T K_{g x} ω \\ \frac{1}{2} T false(K_{g y} + K_{g z} false) ω_{i e} \cos φ \\ \frac{1}{2} T false(K_{g y} + K_{g z} false) ω_{i e} \sin φ \end{matrix}]

…”

Section: Rotary Inertial Navigation Systemmentioning

confidence: 99%

“…However, only few researches have been conducted on rotary INS based on low-cost MEMS IMU, which has great potential applications in the near future and is the focus of this paper. A rotary MEMS-based INS has the potential to significantly increase the time period that it can bridge GNSS outages for GNSS/MEMS IMU integrated systems [17,18]. It also makes MEMS IMU possible to maintain much longer autonomous navigation performance as a self-contained navigation system when there is no external aiding available.…”

Section: Introductionmentioning

confidence: 99%

“…However for MEMS IMU, due to their significant bias instability, scale factor, installation errors, as well as noise, how can the application of the rotation modulation technique help reduce the navigation errors is not clear so far which requires investigation. Although the idea of MEMS-based rotary INS had already been proposed, it was verified only by simulations in previous research [17]. Real rotated tests must be conducted in order to fully investigate the feasibility of MEMS-based rotary systems for practical applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MEMS IMU Error Mitigation Using Rotation Modulation Technique

Sun

Gao

2016

Sensors

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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.

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δ ω_{S F}^{n} = C_{s}^{n} S_{g} ω_{i s}^{s} = [\begin{matrix} K_{g x} ω \\ K_{g y} false(ω_{i e} \cos φ \frac{1 + \cos 2 ω t}{2} + ω_{i e} \sin φ \frac{\sin 2 ω t}{2} false) \\ - K_{g z} false(- ω_{i e} \cos φ \frac{1 - \cos 2 ω t}{2} + ω_{i e} \sin φ \frac{\sin 2 ω t}{2} false) \\ K_{g y} false(ω_{i e} \cos φ \frac{\sin 2 ω t}{2} + ω_{i e} \sin φ \frac{1 - \cos 2 ω t}{2} false) \\ + K_{g z} false(- ω_{i e} \cos φ \frac{\sin 2 ω t}{2} + ω_{i e} \sin φ \frac{1 + \cos 2 ω t}{2} false) \end{matrix}]

true \int_{0}^{T} δ ω_{S F}^{n} d t = [\begin{matrix} T K_{g x} ω \\ \frac{1}{2} T false(K_{g y} + K_{g z} false) ω_{i e} \cos φ \\ \frac{1}{2} T false(K_{g y} + K_{g z} false) ω_{i e} \sin φ \end{matrix}]

…”

Section: Rotary Inertial Navigation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MEMS IMU Error Mitigation Using Rotation Modulation Technique

Sun

Gao

2016

Sensors

Self Cite

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show abstract

“…The measurement calibration using a turn table has been applied not only to different types of inertial sensors such as accelerometers 12, 13 and gyroscopes, 14, 15 but also to a group of sensors in for instance the Inertial Measurement Unit (IMU). 16,17,18 A customized two-axis CT operated by the section Control and Simulation of the Faculty of Aerospace Engineering at Delft University of Technology, produces the reference motion input for this study. The CT and sensor's Data Acquisition System (DAS) instruments are shown in Figure 2.…”

Section: Iia Angular Accelerometer and Data Acquisition Systemmentioning

confidence: 99%

Development of a New Method for Calibrating an Angular Accelerometer Using a Calibration Table

Jatiningrum

Visser

et al. 2015

AIAA Guidance, Navigation, and Control Conference

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Angular Accelerometers (AA) directly measure angular accelerations, in contrast to the traditional approach which requires calculating the angular accelerations by differentiating the angular rates from rate gyroscopes. Whereas very accurate and robust, AA are physical measuring devices, which means that they could have construction and alignment imperfections that affect their outputs. In this paper, AA sensor's output was verified in order to meet a standard reference, achieved by means of an off-line calibration method. To begin with, a laboratory measurement with a customized input sequence was conducted using a high precision calibration table as a reference. The angular acceleration reference signal, however, was not measured directly by the calibration table, but instead calculated from a position reference using a second order differentiator. Inherently, the noise in the position measurements was significantly amplified, leading to an angular acceleration reference that cannot be used directly. A non-linear extended state observer was developed to estimate a more accurate acceleration reference. Subsequently, a calibration model covering the entire domain was identified based on a least-squares approach. This paper improves calibration table angular acceleration data and provides a method to calibrate the new type of angular acceleration sensor.

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“…1,2 Rotating inertial measurement units (IMU) periodically can bound the free propagation of the INS error introduced by gyro drift. 3,4 Thus, this method is applied to improve the precision of FOG INS. As a single-axis rotary INS has an effect on only two gyros, 5,6 one more rotation axis should be added at least to reduce the impact of all three gyros and achieve higher precision of navigation results.…”

Section: Introductionmentioning

confidence: 99%

Self-calibration method based on navigation in high-precision inertial navigation system with fiber optic gyro

Wang

Zhang

et al. 2014

Opt. Eng

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Abstract. A rotary inertial navigation system requires higher calibration accuracy of some error parameters owing to rotation. Conventional multiposition and rotation calibration methods are limited, for they do not consider sensors' actual operating condition. In order to achieve these parameters' values as closely as possible to their true values in application, their influence on navigation is analyzed, and a relevant new calibration method based on a system's velocity output during navigation is designed for the vital error parameters, including inertial sensors' installation errors and the scale factor error of fiber optic gyro. Most importantly, this approach requires no additional devices compared to the conventional method and costs merely several minutes. Experimental results from a real dual-axis rotary fiber optic gyro inertial navigation system demonstrate the practicability and higher precision of the suggested approach. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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MEMS-based rotary strapdown inertial navigation system

Cited by 63 publications

References 3 publications

MEMS IMU Error Mitigation Using Rotation Modulation Technique

MEMS IMU Error Mitigation Using Rotation Modulation Technique

Development of a New Method for Calibrating an Angular Accelerometer Using a Calibration Table

Self-calibration method based on navigation in high-precision inertial navigation system with fiber optic gyro

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