INS Fine Alignment With Low-Cost Gyroscopes: Adaptive Filters for Different Measurement Types

Klein, Itzik; Bar‐Shalom, Yaakov

doi:10.1109/access.2021.3079381

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…In 2010, Iozan, and Collin, reported achieving 1-degree accuracy in determining true North orientation with a low-cost MEMS gyro, while accounting for small errors such as g-sensitivity and cross-axis coupling [31,32]. Among other works over the past decade, Ali (2011) presented a second-order divided difference filter (DDF) [33], Du (2016) introduced a disturbance observer-based Kalman filter (DOBKF) [34], and Klein (2018) demonstrated how kinematic constraints, when coupled with appropriate observability analysis, can effectively enhance unobserved states [35][36][37].…”

Section: B Filtering-basedmentioning

confidence: 99%

A Learning-Based Approach for Bias Elimination in Low-Cost Gyroscopes

Engelsman

Klein

2022

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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Inertial navigation systems (INS) are widely used in both manned and autonomous platforms. One of the most critical tasks prior to their operation is to accurately determine their initial alignment while stationary, as it forms the cornerstone for the entire INS operational trajectory. While lowperformance accelerometers can easily determine roll and pitch angles (leveling), establishing the heading angle (gyrocompassing) with low-performance gyros proves to be a challenging task without additional sensors. This arises from the limited signal strength of Earth's rotation rate, often overridden by gyro noise itself. To circumvent this deficiency, in this study we present a practical deep learning framework to effectively compensate for the inherent errors in low-performance gyroscopes. The resulting capability enables gyrocompassing, thereby eliminating the need for subsequent prolonged filtering phase (fine alignment). Through the development of theory and experimental validation, we demonstrate that the improved initial conditions establish a new lower error bound, bringing affordable gyros one step closer to being utilized in high-end tactical tasks.

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Section: B Filtering-basedmentioning

confidence: 99%

A Learning-Based Approach for Bias Elimination in Low-Cost Gyroscopes

Engelsman

Klein

2022

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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“…For low-cost gyros as a main module of MEMS IMU, the yaw angle is usually subject to divergence in the traditional methods with only the velocity measurements, due to the weak observability of the yaw angle in low dynamic scenarios [31,32]. Therefore, the yaw angle should also be introduced into the measurement update.…”

Section: Introductionmentioning

confidence: 99%

Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

Zhang,

Tang,

Deng

et al. 2023

Meas. Sci. Technol.

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The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna GNSS baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extend Kalman filtering (REKF) is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by Institute of Geodesy and Geophysics (IGG)-Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s-1 and 1.5 m s-1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.

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