Parameter Identification Method for SINS Initial Alignment under Inertial Frame

Han, Xue; Guo, Xiaoqing; Zhou, Zebing

doi:10.1155/2016/5301242

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Unfortunately, due to big random drifts of the gyroscopes in Micro Electro Mechanical System (MEMS) inertial sensors, the misalignment angles become too large. Thus, the assumption that the angles are small cannot accurately describe the error statics in the linear alignment model [3]. Hence, it is important to investigate the nonlinear alignment representation when the misalignment angles are not small.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Alhassan,

Ghahremani

2021

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This paper proposes a new approach called the Predictive Kalman Filter (PKF) which predicts and compensates model errors of inertial sensors to improve the accuracy of static alignment without the use of external assistance. The uncertain model error is the main problem in the field as the Micro Electro Mechanical System (MEMS) inertial sensors have bias which change over time, and these errors are not all observable. The proposed filter determines an optimal equivalent model error by minimizing a quadratic penalty function without augmenting the system state space. The optimization procedure enables the filter to decrease both model uncertainty and external disturbances. The paper first presents the complete formulation of the proposed filter. Then, a nonlinear alignment model with a large misalignment angle is considered. Experimental results demonstrate that the new method improves the accuracy and rapidness of the alignment process as the convergence time is reduced from 550 s to 50 s, and the azimuth misalignment angle correctness is decreased from 52 ± 47 to 4 ± 0.02 .

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Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Alhassan,

Ghahremani

2021

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“…The initial attitude, obtained by initial alignment, is therefore significant for the achievement of high-precision navigation. There are two important indexes for the evaluation of initial-alignment performance: the alignment precision and the convergence speed [ 3 , 4 ]. In recent years, many researchers have been devoted to improving the performance of initial alignment, and a series of valuable methods have been proposed [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Coarse-Alignment Method Based on the Optimal-REQUEST Algorithm

Zhu

Zhang

2018

Sensors

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In this paper, we proposed a coarse-alignment method for strapdown inertial navigation systems based on attitude determination. The observation vectors, which can be obtained by inertial sensors, usually contain various types of noise, which affects the convergence rate and the accuracy of the coarse alignment. Given this drawback, we studied an attitude-determination method named optimal-REQUEST, which is an optimal method for attitude determination that is based on observation vectors. Compared to the traditional attitude-determination method, the filtering gain of the proposed method is tuned autonomously; thus, the convergence rate of the attitude determination is faster than in the traditional method. Within the proposed method, we developed an iterative method for determining the attitude quaternion. We carried out simulation and turntable tests, which we used to validate the proposed method’s performance. The experiment’s results showed that the convergence rate of the proposed optimal-REQUEST algorithm is faster and that the coarse alignment’s stability is higher. In summary, the proposed method has a high applicability to practical systems.

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“…According to the characteristics of the temporary anchoring UUV in the polar region, the initial UUV alignment corresponds to the rocking base type. Polar coarse alignment process and polar fine alignment process constitute a complete polar initial alignment process [8,9]. …”

Section: Introductionmentioning

confidence: 99%

A Polar Initial Alignment Algorithm for Unmanned Underwater Vehicles

Yan

Wang

et al. 2017

Sensors

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Due to its highly autonomy, the strapdown inertial navigation system (SINS) is widely used in unmanned underwater vehicles (UUV) navigation. Initial alignment is crucial because the initial alignment results will be used as the initial SINS value, which might affect the subsequent SINS results. Due to the rapid convergence of Earth meridians, there is a calculation overflow in conventional initial alignment algorithms, making conventional initial algorithms are invalid for polar UUV navigation. To overcome these problems, a polar initial alignment algorithm for UUV is proposed in this paper, which consists of coarse and fine alignment algorithms. Based on the principle of the conical slow drift of gravity, the coarse alignment algorithm is derived under the grid frame. By choosing the velocity and attitude as the measurement, the fine alignment with the Kalman filter (KF) is derived under the grid frame. Simulation and experiment are realized among polar, conventional and transversal initial alignment algorithms for polar UUV navigation. Results demonstrate that the proposed polar initial alignment algorithm can complete the initial alignment of UUV in the polar region rapidly and accurately.

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Parameter Identification Method for SINS Initial Alignment under Inertial Frame

Cited by 5 publications

References 23 publications

Metrology and Measurement Systems

Metrology and Measurement Systems

A Coarse-Alignment Method Based on the Optimal-REQUEST Algorithm

A Polar Initial Alignment Algorithm for Unmanned Underwater Vehicles

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