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Underwater Terrain Positioning Method Using Maximum a Posteriori Estimation and PCNN Model

Published online by Cambridge University Press:  04 March 2019

Pengyun Chen*
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
Pengfei Zhang
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
Teng Ma
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Peng Shen
Affiliation:
(National Deep Sea Center, Qingdao 266237, China)
Ye Li
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Rupeng Wang
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Yue Han
Affiliation:
(Modern Education Information Centre, Taiyuan Tourism College, Taiyuan 030032, China)
Lizhou Li
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)

Abstract

Conventional underwater navigation and positioning methods for Autonomous Underwater Vehicles (AUVs) either require the installation of acoustic arrays, which make AUVs less independent, or result in cumulative errors. This paper proposes an Underwater Terrain Positioning Method (UTPM) using Maximum a Posteriori (MAP) estimation and a Pulse Coupled Neural Network (PCNN) model for highly accurate navigation by AUVs. The PCNN model is used as a secondary discriminant to effectively identify pseudo-anchor points in flat terrain feature areas and to find the true positioning point, which significantly improves the matching positioning accuracy in these areas. Simulation results show that the proposed method effectively corrects Inertial Navigation System (INS) cumulative errors and has high matching positioning accuracy, which satisfy the requirements of AUV underwater navigation and positioning.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 

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