Backtracking Integration for Fast Attitude Determination-Based Initial Alignment

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

Section: Introductionmentioning

confidence: 99%

Error Analysis of Accelerometer- and Magnetometer-Based Stationary Alignment

Silva

Paiva

Carvalho

2021

“…Accordingly, some drawbacks such as slow convergence, vulnerability to divergence and low alignment accuracy have to be faced with Vb-based IMADA. In Reference [29], the backward process is proposed to improve the rapidity of IMADA, thus saving the alignment time. For the problem of anti-interference, the low-pass FIR filters is designed to eliminate the interferential acceleration [30].…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [32], the gyrocompass horizontal alignment algorithm is employed to attenuate the OD noises, thereby improving the robustness of the IMADA. For the third drawback of IMADA aided by Vb, one of the reasons is the noise errors of sensors, which is also the mainstream of research orientation [25,26,27,28,29,30,31,32]. However, few researches are presented for the two other reasons, namely, the calculation errors and the principled model errors.…”

Section: Introductionmentioning

confidence: 99%

Multistage Attitude Determination Alignment for Velocity-Aided In-Motion Strapdown Inertial Navigation System with Different Velocity Models

Gao

Liu

2019

A novel multistage attitude determination alignment algorithm with different velocity models is proposed to implement the alignment process of in-motion attitude determination alignment (IMADA) aided by the ground velocity expressed in body frame () in this paper. Normally, The-based IMADA is used to achieve the coarse alignment for strapdown inertial navigation system (SINS). The higher the coarse alignment accuracy, the better initial condition can be achieved to guarantee the performance of the subsequent fine alignment. Consider the influence of the principal model errors and the calculation errors on the alignment accuracy in traditional-based IMADA, this paper deals with a novel alignment algorithm by integrating two different velocity-based IMADAs and the multiple repeated alignment processes. The power of this novel alignment algorithm lies in eliminating the principal model errors and decreasing the calculation errors. Then, the higher alignment accuracy is achieved. Simulations and vehicle experiment are performed to demonstrate the validity of the proposed algorithm.

“…For these aforementioned drawbacks of the Vb-aided IMADA, many works have been carried out. In [23,24], a fast IMADA assisted by the backward navigation algorithm is presented, thereby lengthening the SINS data equivalently and shortening the alignment time. Moreover, the low-pass finite impulse response (FIR) or infinite impulse response (IIR) filters have also been proposed to eliminate the noise disturbance of sensors, including the accelerometer sensor and the external aiding sensors [25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Improved Multistage In-Motion Attitude Determination Alignment Method for Strapdown Inertial Navigation System

Qiao

Liu

Meng

et al. 2019

This paper derives an improved multistage in-motion attitude determination alignment (IMADA) for strapdown inertial navigation system, which integrates the traditional IMADA and the designed dual velocity-modeling IMADA, as well as the multiple repeated alignment process, to address the principled model errors and the calculation errors of traditional Vb-aided IMADA. With the proposed algorithm, not only the designed drawbacks of traditional Vb-based IMADA can be solved, but also the degradation phenomenon of high-level alignment for multistage IMADA would be largely less. Moreover, the degradation of the alignment accuracy with the vehicle velocity is also removed. Finally, the 30 groups of car-mounted experiments and the Monte Carlo simulation experiments with the navigation-grade SINS are carried out to demonstrate the validity of the proposed algorithm. The results show that the number of the heading degradation of the second-level alignment is reduced to 10 as compared the traditional number 20. Moreover, the alignment accuracy of heading is improved by 23%. Even with the different speeds of 20 m/s, 60 m/s, 80 m/s, the heading alignment accuracies are 1.3063°, 1.3102°, 1.3564° and are still almost the same.