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

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...

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Bibliographic Details
Published inJournal of navigation Vol. 72; no. 5; pp. 1233 - 1253
Main Authors Chen, Pengyun, Zhang, Pengfei, Ma, Teng, Shen, Peng, Li, Ye, Wang, Rupeng, Han, Yue, Li, Lizhou
Format Journal Article
LanguageEnglish
Published Cambridge, UK Cambridge University Press 01.09.2019
Subjects
Accuracy
Acoustic arrays
Acoustics
Aircraft
Anchors
Autonomous underwater vehicles
Computer simulation
Errors
Expected values
Inertial navigation
Matching
Measurement techniques
Methods
Navigation
Navigation systems
Neural networks
Terrain
Underwater acoustics
Underwater vehicles
Terrain Matching Positioning
Maximum a Posteriori estimation
Autonomous Underwater Vehicle
Pulse Coupled Neural Network
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Summary: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.
ISSN:0373-4633
1469-7785
DOI:10.1017/S0373463319000067