Machine Learning Methods for SAR Interference Mitigation

Yan Huang; Lei Zhang; Jie Li; Mingliang Tao; Zhanye Chen; Wei Hong

doi:10.1007/978-3-031-21225-3_6

Machine Learning Methods for SAR Interference Mitigation

Yan Huang, Lei Zhang, Jie Li, Mingliang Tao, Zhanye Chen, Wei Hong

School of Electronics and Information

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Interference mitigation problem is a major issue in active remote sensing especially via a wideband synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This chapter provides a comprehensive study of the interference mitigation techniques applicable for an SAR system. Typical signal models for various interference types are provided, together with many illustrative examples from real SAR data. In addition, advanced signal processing techniques, specifically machine learning methods, for suppressing interferences are analyzed in detail. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends is provided from the perspective of cognitive and deep learning frameworks.

Original language	English
Title of host publication	Springer Optimization and Its Applications
Publisher	Springer
Pages	113-146
Number of pages	34
DOIs	https://doi.org/10.1007/978-3-031-21225-3_6
State	Published - 2022

Publication series

Name	Springer Optimization and Its Applications
Volume	199
ISSN (Print)	1931-6828
ISSN (Electronic)	1931-6836

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10.1007/978-3-031-21225-3_6

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title = "Machine Learning Methods for SAR Interference Mitigation",

abstract = "Interference mitigation problem is a major issue in active remote sensing especially via a wideband synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This chapter provides a comprehensive study of the interference mitigation techniques applicable for an SAR system. Typical signal models for various interference types are provided, together with many illustrative examples from real SAR data. In addition, advanced signal processing techniques, specifically machine learning methods, for suppressing interferences are analyzed in detail. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends is provided from the perspective of cognitive and deep learning frameworks.",

author = "Yan Huang and Lei Zhang and Jie Li and Mingliang Tao and Zhanye Chen and Wei Hong",

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AU - Zhang, Lei

AU - Li, Jie

AU - Tao, Mingliang

AU - Chen, Zhanye

AU - Hong, Wei

PY - 2022

Y1 - 2022

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AB - Interference mitigation problem is a major issue in active remote sensing especially via a wideband synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This chapter provides a comprehensive study of the interference mitigation techniques applicable for an SAR system. Typical signal models for various interference types are provided, together with many illustrative examples from real SAR data. In addition, advanced signal processing techniques, specifically machine learning methods, for suppressing interferences are analyzed in detail. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends is provided from the perspective of cognitive and deep learning frameworks.

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Machine Learning Methods for SAR Interference Mitigation

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