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Research On Error Identification Algorithm For Small AUV Integrated Navigation System

Posted on:2018-11-16Degree:MasterType:Thesis
Country:ChinaCandidate:X B WangFull Text:PDF
GTID:2322330542487187Subject:Ships and marine structures, design of manufacturing
Abstract/Summary:PDF Full Text Request
As an important tool for mankind to explore the unknown ocean world,the development of Autonomous Underwater Vehicle(AUV)has attracted more and more attention from the world.But high-precision navigation technology has become an important challenge to limit the development of underwater vehicle technology.According to the actual situation of "AUV-R",a new integrated navigation system based on ranging sonar and inland ECDIS is proposed,the error of the navigation system can be identified and modified by the system.Here is the main work of the paper.1.Design an integrated navigation system for small AUV,analyze the performance of the sensors involved in the integrated navigation system and put forward the corresponding dat a processing algorithms.The overall framework of the integrated navigation system is constructed.2.Analyze the scale factor error and installation angle deviation of DVL,and put forward the discrete and itegral error identification algorithm.Carry on the simulation experiment of the integral error identification algorithm.Analyze and identify the magnetic deviation of TC M5,put forward segmented identify algorithm of it,and verify it through experiment.Identify the installation angle deviation of ranging sonar,and compensate for the speed-induced ranging errors.3.This paper proposes an auxiliary positioning algorithm based on ranging sonar and inland ECDIS.Propose and validate the feasibility of the ranging sonar weighted round robin working mode.The improved UKF algorithm is proposed by using the auxiliary positioning algorithm,and the initial position error,velocity error and installation angle of AUV are identified by the auxiliary information.4.Build a semi-physical simulation platform,v erify the filtering effect of the sensor data and the dead reckoning algorithm through the platform.The simulation results show that the algorithm proposed in this paper has a good effect and can improve the accuracy of the navigation system.
Keywords/Search Tags:AUV, integrated navigation, dead reckoning, error identification, Kalman filter
PDF Full Text Request
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