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

Xianjun Liu (Department of Instrument Science and Engineering, Southeast University, Nanjing, China)
Xixiang Liu (School of Instrument Science and Engineering, Southeast University, Nanjing, China)
Hang Shen (Department of Instrument Science and Engineering, Southeast University, Nanjing, China)
Peijuan Li (Industrial Center, Nanjing Institute of Technology, Nanjing, China)
Tongwei Zhang (National Deep Sea Center, Qingdao, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 8 January 2021

Issue publication date: 19 February 2021

130

Abstract

Purpose

Motivated by the problems that the positioning error of strap-down inertial navigation system (SINS) accumulates over time and few sensors are available for midwater navigation, this paper aims to propose a self-aided SINS scheme for the spiral-diving human-occupied vehicle (HOV) based on the characteristics of maneuvering pattern and SINS error propagation.

Design/methodology/approach

First, the navigation equations of SINS are simultaneously executed twice with the same inertial measurement unit (IMU) data as input to obtain two sets of SINS. Then, to deal with the horizontal velocity provided by one SINS, a delay-correction high-pass filter without phase shift and amplitude attenuation is designed. Finally, the horizontal velocity after processing is used to integrate with other SINS.

Findings

Simulation results indicate that the horizontal positioning error of the proposed scheme is less than 0.1 m when an HOV executes spiral diving to 7,000 meters under the sea and it is inherently able to estimate significant sensors biases.

Originality/value

The proposed scheme can provide a precise navigation solution without error growth for spiral-diving HOV on the condition that only IMU is required as a navigation sensor.

Keywords

Citation

Liu, X., Liu, X., Shen, H., Li, P. and Zhang, T. (2021), "Self-aided SINS for spiral-diving human-occupied vehicle in midwater", Assembly Automation, Vol. 41 No. 1, pp. 106-115. https://doi.org/10.1108/AA-05-2020-0072

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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