Self-aided SINS for spiral-diving human-occupied vehicle in midwater

Liu, Xianjun; Liu, Xixiang; Shen, Huanfeng; Li, Peijuan; Zhang, Tongwei

doi:10.1108/aa-05-2020-0072

Cited by 5 publications

(4 citation statements)

References 26 publications

(47 reference statements)

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“…2, which includes two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to execute a specific trajectory so as to realize Self-Aided SINS [20], which is based on error propagation of SINS and maneuvering characteristics of vehicle. Combined with DVL measurements (working in water tracking mode) and precise navigation solution of Self-Aided SINS, local ocean current velocity can be estimated effectively and efficiently.…”

Section: Problem Analysis Of Sins/dvl Integrated Navigationmentioning

confidence: 99%

“…Considering that SINS horizontal velocity contains high-frequency true velocity based on trajectory constraints and low-frequency velocity error based on working mechanism of SINS, a delay correction-high-pass filter (DC-HPF) will be designed to handle horizontal velocity of SINS1. Finally, conventional KF technology is utilized to integrate obtained high-frequency true velocity with SINS2 to obtain navigation results without drift error, thus realizing the so-called Self-Aided SINS navigation scheme without requiring other external auxiliary navigation sensors [20].…”

Section: Self-aided Sins Based On Trajectory Constraintsmentioning

confidence: 99%

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SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

et al. 2021

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Motivated by the problem that water-track Doppler Velocity Log (DVL) cannot effectively suppress the error accumulation of strap-down inertial navigation system (SINS), this paper proposes a novel SINS/DVL integrated navigation algorithm for deep and long cruising range Human Occupied Vehicle (HOV). Such algorithm decomposes the navigation process into two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to perform horizontal circular motion for several minutes to realize Self-Aided SINS. Combining the precise navigation solutions provided by Self-Aided SINS and measurements from DVL with water track, recursive least square (RLS) algorithm is adopted to estimate heading misalignment angle between SINS and DVL and horizontal ocean current velocity. In the navigation mode, both horizontal ocean current velocity obtained in the alignment mode and DVL measurements are utilized to assist SINS, thus enabling SINS/DVL/Current integrated navigation. A square trajectory with a navigation-grade inertial measurement unit (IMU) is simulated to evaluate the proposed SINS/DVL integrated navigation algorithm. Simulation results show that the proposed SINS/DVL integrated navigation is capable of suppressing SINS error divergence effectively and efficiently. In addition, the feasibility and effectiveness of Self-Aided SINS based on horizontal circular motion is also verified by field test with real IMU data.

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Section: Problem Analysis Of Sins/dvl Integrated Navigationmentioning

confidence: 99%

Section: Self-aided Sins Based On Trajectory Constraintsmentioning

confidence: 99%

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

et al. 2021

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“…However, this method could be better and still needs to improve. Liu et al proposed a concept of a self-aided navigation system, using only information from the IMU itself [28], and Wu et al made some optimizations on this basis [29]. Ben et al proposed a dual-state filter algorithm to solve the problem of degradation of the accuracy of the SINS/DVL integrated navigation system due to ocean currents [30].…”

Section: Introductionmentioning

confidence: 99%

Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity

Huang,

Liu,

Shao

et al. 2023

Remote Sensing

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The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.

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“…Strapdown inertial navigation systems (SINS) are widely used in the field of navigation with outstanding advantages such as autonomy and concealment (Fang and Gong, 2010; Liu et al , 2021; Dinc and Hajiyev, 2015). The inertial navigation system must perform initial alignment before entering the working state.…”

Section: Introductionmentioning

confidence: 99%

A transfer alignment algorithm based on combined double-time observation of velocity and attitude

Sheng

Liu²,

Wang³

et al. 2022

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Purpose This paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate. Design/methodology/approach Based on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed. Findings The results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment. Originality/value Calibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

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Self-aided SINS for spiral-diving human-occupied vehicle in midwater

Cited by 5 publications

References 26 publications

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity

A transfer alignment algorithm based on combined double-time observation of velocity and attitude

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