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Multi-Sensor, Adjustable-Period Integrated Navigation Method Based on Multi-Stage Signal Trigger for Underwater Vehicles

Published online by Cambridge University Press:  29 August 2017

Yanshun Zhang
Affiliation:
(School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China)
Baichao Ding*
Affiliation:
(School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China)
Xiaojuan Huang
Affiliation:
(School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China)
Tao Yang
Affiliation:
(China Academy of Aerospace Aerodynamics, Beijing, 100074, China)
Xiaodong Liu
Affiliation:
(Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China)
*
(E-mail: mealery@163.com)

Abstract

For underwater vehicle navigation sensors, the output signal periods are different and time-varying. This would result in the decline of precision, and even wrong results. To deal with the problem, this paper puts forward a multi-sensor, adjustable-period integrated navigation method based on multi-stage signal trigger. This method considers the valid signals of a Doppler Velocity Log (DVL) as the trigger signals of a Dead-Reckoning (DR) navigation program. It also considers the valid signals of an acoustic positioning sensor as the trigger signals of the integrated navigation program. In this method, it can adjust the filtering period in real time. According to the time label of signals, this method actualises the time-space alignment of sensors. Then it conducts DR navigation and integrated navigation. The method can not only utilise the valid signals of each sensor sufficiently but also fuses the data based on time-space alignment efficiently. Sea trial data shows that when the output signal periods are certain, navigation precision of the method in this paper is better than a non-adjustable-period filtering method. Moreover, in poor conditions, it can also attain a high precision.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2017 

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