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STOCHASTIC MATCHED FIELD PROCESSING USING DIRECTED RIEMANNIAN DISTANCE
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The Matched Field Processing (MFP) plays a crucial role in modern passive SONAR system since its effectiveness of underwater
source localization. Their applications are but not limited to floating boat detecting, submarine detecting, fish finding as well as
ocean environmental parameters determining. In the past, the stochastic matched field processing (SMFP) are derived on the basis
of Riemannian distances (RDs) which were calculated using isometric mappings (IMs). In this paper, a new STMP is provided using
directed RDs which were obtained by solving the geodesic equations directly instead of using IMs. In addition, we exploit the symmetric
property of Riemannian manifold to solve the geodesic equation in the simple manner. The performance of the proposed STMP
outperformed to that of standard algorithm at the expense of a little more of computation.
Title: STOCHASTIC MATCHED FIELD PROCESSING USING DIRECTED RIEMANNIAN DISTANCE
Description:
The Matched Field Processing (MFP) plays a crucial role in modern passive SONAR system since its effectiveness of underwater
source localization.
Their applications are but not limited to floating boat detecting, submarine detecting, fish finding as well as
ocean environmental parameters determining.
In the past, the stochastic matched field processing (SMFP) are derived on the basis
of Riemannian distances (RDs) which were calculated using isometric mappings (IMs).
In this paper, a new STMP is provided using
directed RDs which were obtained by solving the geodesic equations directly instead of using IMs.
In addition, we exploit the symmetric
property of Riemannian manifold to solve the geodesic equation in the simple manner.
The performance of the proposed STMP
outperformed to that of standard algorithm at the expense of a little more of computation.
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