Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

Yang, Bo; Xu, Xiaosu; Zhang, Tao; Sun, Jin; Liu, Xinyu

doi:10.1155/2017/5917917

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Due to the interference from real-world environments, SINS and DVL are affected by non-Gaussian noise, leading to measurement noise that does not adhere to the previous Gaussian assumption [38]. In such cases, the performance of the Kalman Filter (KF) may degrade and it is advisable to use H∞ technology to improve the robustness of the filter [39]. It is worth noting that different Kalman filtering methods have their own advantages and disadvantages.…”

Section: Localization With the Sins/dr Combination Methodsmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

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Section: Localization With the Sins/dr Combination Methodsmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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“…Initial alignment with large azimuth misalignment angle is the key technology of SINS [1,2]. According to the characteristics of the nonlinear error model, nonlinear EKF and UKF are usually applied on tradition.…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

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“…A comparable method is recommended in [8] for SINS alignment on a swing base. [9] proposes a filter based on an unscented Kalman filter (UKF) to handle the uncertain noise during determining the initial misalignment angles. However, the computational cost of this method is higher than in the other nonlinear filters.…”

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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Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

Cited by 6 publications

References 23 publications

A Review of Underwater Robot Localization in Confined Spaces

A Review of Underwater Robot Localization in Confined Spaces

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Metrology and Measurement Systems

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