Machine Learning Methods for SAR Interference Mitigation

Yan Huang (), Lei Zhang (), Jie Li (), Mingliang Tao (), Zhanye Chen () and Wei Hong ()
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Yan Huang: Southeast University
Lei Zhang: Sun Yat-san University
Jie Li: Nanjing University of Aeronautics and Astronautics
Mingliang Tao: Northwestern Polytechnical University
Zhanye Chen: Chongqing University
Wei Hong: Southeast University

A chapter in Synthetic Aperture Radar (SAR) Data Applications, 2022, pp 113-146 from Springer

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-21225-3_6

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

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