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Article

Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods

College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China
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Author to whom correspondence should be addressed.
Academic Editors: Andrzej Stateczny, Witold Kazimierski and Krzysztof Kulpa
Remote Sens. 2021, 13(18), 3689; https://doi.org/10.3390/rs13183689
Received: 15 July 2021 / Revised: 31 August 2021 / Accepted: 10 September 2021 / Published: 15 September 2021
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time. View Full-Text
Keywords: RCS reconstruction; DE–GTD model; sparse iterative covariance-based estimation (SPICE) algorithm; scattering parameters estimation; frequency-hopping pattern RCS reconstruction; DE–GTD model; sparse iterative covariance-based estimation (SPICE) algorithm; scattering parameters estimation; frequency-hopping pattern
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MDPI and ACS Style

Li, Y.; Zhang, W.; Tian, B.; Lin, W.; Liu, Y. Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods. Remote Sens. 2021, 13, 3689. https://doi.org/10.3390/rs13183689

AMA Style

Li Y, Zhang W, Tian B, Lin W, Liu Y. Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods. Remote Sensing. 2021; 13(18):3689. https://doi.org/10.3390/rs13183689

Chicago/Turabian Style

Li, Yingjun, Wenpeng Zhang, Biao Tian, Wenhao Lin, and Yongxiang Liu. 2021. "Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods" Remote Sensing 13, no. 18: 3689. https://doi.org/10.3390/rs13183689

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