Open AccessRange‐dependent semi‐parametric autofocusing for manoeuvring targets inverse synthetic aperture imagery
Author(s) -
Sun Xiping,
Zhang Lei,
Wang Guanyong,
Sheng Jialian
Publication year - 2020
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2020.0124
Subject(s) - autofocus , inverse synthetic aperture radar , parametric statistics , computer science , computer vision , synthetic aperture radar , artificial intelligence , robustness (evolution) , radar imaging , algorithm , radar , focus (optics) , mathematics , optics , physics , telecommunications , statistics , biochemistry , chemistry , gene
Phase adjustment with the autofocus process is essential to inverse synthetic aperture radar (ISAR) imagery of manoeuvring targets. Conventional parametric and non‐parametric autofocus methods usually degrade in front of error model mismatch and noise interference. Especially, the target manoeuvre also induces higher order phases that have the spatial‐variant characteristic. In this study, a range‐dependent semi‐parametric autofocus algorithm for ISAR imagery is proposed. The range‐variant phase is modelled semi‐parametrically with a discrete cosine transform kernel. Implemented by the maximum sharpness optimisation, the algorithm well adapts to complex phase forms and enhances the robustness to strong noise. Accelerated solver to the optimisation is also introduced. Simulation and real data experiments confirm the advantages of the range‐dependent semi‐parametric autofocus algorithm compared with conventional algorithms.