A Data-Driven Approach to Modeling Sea Clutter Stochastic Differential Equations

The development of a family of data-driven methods, called dynamic mode decomposition, for modeling the behavior of dynamical systems through the approximation of the associated Koopman operator, has led to a rapid increase in the related research. Separately, the modeling and algorithm development...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2024-08, Vol.60 (4), p.5312-5321
Main Authors: Pici, Caden J., Ray, Asok, Kompella, Sastry, Narayanan, Ram M.
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Anomaly detection
Clutter
Differential equations
dynamic mode decomposition (DMD)
Dynamic models
Dynamical systems
dynamical systems modeling
Heuristic algorithms
Kalman filters
Koopman operator
Mathematical models
Modelling
Operators (mathematics)
Parameter estimation
Parameter identification
Probability density functions
Radar data
Radar detection
Radar measurement
Radar scattering
sea clutter
Sea state
Sea states
Sea surface
stochastic differential equations (SDE)
Stochastic processes
Target detection
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Summary:The development of a family of data-driven methods, called dynamic mode decomposition, for modeling the behavior of dynamical systems through the approximation of the associated Koopman operator, has led to a rapid increase in the related research. Separately, the modeling and algorithm development for target detection in the presence of sea clutter often involves probability density function descriptions of the amplitude process, which ignores time dependency in data. In this article, we combine these data-driven methods with a stochastic differential equation model of radar scattering from the sea surface for the purpose of sea-state change detection. This approach relies on building a dynamic model of sea clutter directly from the radar measurements, without the need to estimate parameters of underlying equations. Using this model, an anomaly detection scheme is demonstrated using a Kalman filtering approach constructed from the Koopman model that is able to identify changes in the sea state.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3394456