Optimization-based self-alignment method for stationary SINS with geographical latitude uncertainty

Under the marine mooring conditions, the inertial navigation system is often disturbed by angular and linear motions, and it is difficult to perform self-alignment with high accuracy. To solve this problem, an anti-disturbance self-alignment method for dual-axis rotational inertial navigation system is proposed. The method transforms the self-alignment problem into an attitude matrix solving problem, so as to deal with the disturbance of angular motion. By analyzing the relationship between the amount of measurement data and the accuracy of self-alignment, the optimal integration time is designed and a partial integral algorithm is proposed. Furthermore, the influence of initial velocity error on self-alignment under linear motion disturbance conditions is analyzed, and an initial velocity compensation method is proposed. Experimental results show that high-accuracy self-alignment can be achieved using the proposed method under the joint disturbance of angular and linear motions.

Section: Introductionmentioning

confidence: 99%

Anti-disturbance self-alignment method for dual-axis rotational inertial navigation system under marine mooring conditions

Lin,

Miao,

Zhou

2024

“…The second category was first proposed by Wu et al [7] and the real-time accuracy evaluation of this method was studied in [8]. The improved methods have been developed in recent years, with the applicability ranging from navigationgrade IMU [9][10][11] to MEMS IMU [12][13][14]. However, this category is essentially a coarse alignment.…”

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

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.

GNSS-assisted optimal alignment method for low-cost SINS motion of vehicle

Yan,

Si,

Chen

et al. 2024

To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.