Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines

Nazar, Imara Mohamed; Aksoy, Mustafa

doi:10.1109/jstars.2023.3293393

Cited by 1 publication

(2 citation statements)

References 37 publications

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“…As a state‐of‐the‐art machine learning technique, SVMs are widely used to perform data classification and regression tasks [23–25]. SVM is a technique based on linear discriminant functions, maximum margin classifiers and the concept referred to as kernel trick [17].…”

Section: Alternative Machine Learning Techniques For Time Delay Estim...mentioning

confidence: 99%

“…In fact, the time delays in SAS can be understood as a mapping relationship between the echoes and the platform motion, which suggests that supervised learning techniques may be effectively utilised in the time delay estimation of SAS. There are many different forms of mapping in supervised learning, including decision trees, logistic regression, kernel machines, support vector machines (SVMs), Bayesian classifiers, and neural networks [14–26]. In recent years, deep neural networks have made remarkable progress in the field of supervised learning [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

Chen,

Chi,

Zhang

et al. 2023

IET Radar Sonar & Navi

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Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

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Section: Alternative Machine Learning Techniques For Time Delay Estim...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

Chen,

Chi,

Zhang

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines

Cited by 1 publication

References 37 publications

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

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