Rotating Parabolic-Reflector Antenna Target in SAR Data: Model, Characteristics, and Parameter Estimation
Author(s) -
Bin Deng,
Hongqiang Wang,
Yuliang Qin,
Sha Zhu,
Xiang Li
Publication year - 2013
Publication title -
international journal of antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.282
H-Index - 37
eISSN - 1687-5877
pISSN - 1687-5869
DOI - 10.1155/2013/583865
Subject(s) - synthetic aperture radar , scattering , radar , estimator , maxima and minima , radar cross section , reflector (photography) , computer science , antenna (radio) , corner reflector , rotation (mathematics) , algorithm , estimation theory , physical optics , physics , optics , artificial intelligence , mathematics , mathematical analysis , telecommunications , light source , statistics
Parabolic-reflector antennas (PRAs), usually possessing rotation, are a particular type of targets of potential interest to the synthetic aperture radar (SAR) community. This paper is aimed to investigate PRA’s scattering characteristics and then to extract PRA’s parameters from SAR returns, for supporting image interpretation and target recognition. We at first obtain both closed-form and numeric solutions to PRA’s backscattering by geometrical optics (GO), physical optics, and graphical electromagnetic computation, respectively. Based on the GO solution, a migratory scattering center model is at first presented for representing the movement of the specular point with aspect angle, and then a hybrid model, named the migratory/micromotion scattering center (MMSC) model, is proposed for characterizing a rotating PRA in the SAR geometry, which incorporates PRA’s rotation into its migratory scattering center model. Additionally, we in detail analyze PRA’s radar characteristics on radar cross-section, high-resolution range profiles, time-frequency distribution, and 2D images, which also confirm the models proposed. A maximal likelihood estimator is developed for jointly solving the MMSC model for PRA’s multiple parameters by optimization. By exploiting the aforementioned characteristics, the coarse parameter estimation guarantees convergency upon global minima. The signatures recovered can be favorably utilized for SAR image interpretation and target recognition
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