Backtracking scheme for single-point self-calibration and rapid in-motion alignment with application to a position and azimuth determining system

To solve the self-alignment problem of strapdown inertial navigation system (SINS) with geographical latitude uncertainty, an optimization-based self-alignment (OSA) method and its improvement for stationary SINS without using the latitude information are proposed. We use only the accelerometer and gyroscope measurements, without the aid of the external latitude information, to determine the Earth rate in the navigation frame. Then we formulate the SINS self-alignment process as a Wahba problem to overcome the disturbances of random noise, and use the estimated Earth rate vector and multiple measurements from the accelerometer and gyroscope to formulate the nonlinear objective function. Moreover, the alignment errors of the OSA method are also presented, based on which we propose a two-position OSA to estimate and compensate the horizontal accelerometer biases to further improve the alignment accuracy. The results of simulation and experiments demonstrate that the proposed OSA method and its improvement perform robust to the noise disturbances and achieve better alignment accuracy than conventional self-alignment methods.

Section: Mathematical Formulationmentioning

confidence: 99%

“…Conventionally, the initial alignment process of SINS consists of two phrases: coarse alignment and fine alignment [13][14][15][16][17][18][19][20]. The coarse alignment is employed to achieve a rough initial attitude with a rapid speed, and then the fine alignment is employed to achieve a more accurate alignment result.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Duan²,

Yuan³

et al. 2022

“…Furthermore, the local-level frame, which is also known as the navigation frame (n frame), is typically chosen as the reference frame in computation of inertial navigation system (INS) in most literatures [18,20]. Although intuitive, it is more computationally complex due to the introduction of the transition matrix from the earth-centered-earth-fixed frame (e frame) to the n frame.…”

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.

“…The initial alignment determines the attitude matrix between the body frame and the navigation frame since the inertial measurement unit (IMU) provides the angular rate and acceleration in the body frame whereas the purpose of navigation is to determine the position of the vehicle in the navigation frame [2,3]. The initial alignment of the SINS is one of the key technologies that affect the accuracy of vehicle navigation, and the accuracy and speed of the initial alignment directly affect the accuracy of the SINS [4], so the initial alignment of a SINS has been a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

In-motion initial alignment method for a laser Doppler velocimeter-aided strapdown inertial navigation system based on an adaptive unscented quaternion H-infinite filter

Xiang¹,

Wang²,

Huang³

et al. 2021

With the advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDV) is expected to replace the odometer (OD) to be combined with a strapdown inertial navigation system (SINS) to form a higher precision integrated navigation system. Since LDV has higher velocity measurement accuracy and data update frequency than OD and Doppler velocity log, LDV is used for the first time to aid SINS in in-motion alignment in this paper. Considering that some approximation used in the alignment model, the uncertainty noise of the sensors during the motion process and the unknown noise parameters during the filter process, an adaptive unscented quaternion H-infinite estimator (AUSQUHE) is proposed in this paper. The proposed AUSQUHE method has high robustness since it combines the advantages of unscented quaternion estimator and H-infinite filter. And the adaptive threshold of H-infinite filter and the adaptive measurement noise covariance matrix are introduced to make the filter adapt to the environment change and accelerate the convergence of errors. The performance of the proposed method is verified by a vehicle field test with normal LDV signal and a vehicle test with LDV signal disturbed by the noise. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than four other compared methods.