RIEMANNIAN MATCHED FIELD PROCESSING
As far as underwater source localization is concerned the Matched Field Processing (MFP) is an effective method. Floating ship localization, submarine localization in military section and fish finding in civilization are considered as the main application of
MFP. Besides, determining environmental parameters such as sound speed profile, bottom topography and array tilt are also developed. Some methods such as empirical mode decomposition, adaptive MFP, compressive MFP and especially stochastic MFP using Riemannian geometry (RMFP) have been introduced recently in order to increase MFP’s reliability and resolution. It seems that the RMFP is the strongest candidate for the future development of MFP since it is inherited the strong foundation of both MFP and Riemannian Geometry. Surprisingly, not only the nature of curvature of sound wave but also the nature of MFP are exploited in RMFP.
The aim of this monograph is introduce RMFP by considering the Riemannian distance instead of Euclidean distance. Two approaches of RMFP construction, i.e., isometric appings and direct Riemannian distance calculation are introduced.
The organization of this monograph is as follows. Two first chapters of this monograph revised the reader about the essential
meaning of Gauss Curvature, Geodesic equation, iso-metric mapping in Riemannian Geometry and the state of the art of MFP.
Chapter 3 presents Riemannian MFP. Chapter 4 concludes the monograph with discussions about the performance of MFP. This monograph is designed for graduated students, scientists and senior engineers who working in the field of underwater acoustic engineering.
We would like to thanks SACLANTC for providing access of SONAR array data. We also express our gratitude to University of
Engineering and Technology (VNUH) for partial financial support this monograph.
Finally, I deeply express my appreciate to my family, especially my father for their patient and love to me.
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