<title>Scattering center parameter estimation using a polynomial model for the amplitude aspect dependence</title>

Parameter Estimation of Attribute Scattering Center Based on Water Wave Optimization Algorithm

Scientific Programming ◽

10.1155/2021/6733510 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Zhangkai Zhou ◽

Yihan Li

Keyword(s):

Parameter Estimation ◽

Water Wave ◽

Single Scattering ◽

High Energy ◽

Sar Image ◽

Original Image ◽

Image Domain ◽

Scattering Center ◽

Parameterized Model ◽

Center Parameter

For the problem of attribute scattering center parameter estimation in synthetic aperture radar (SAR) image, a method based on the water wave optimization (WWO) algorithm is proposed. First, the segmentation and decoupling of high-energy regions in SAR image are performed in the image domain to obtain the representation of a single scattering center. Afterwards, based on the parameterized model of the attribute scattering center, an optimization problem is constructed to search for the optimal parameters of the separated single scattering center. In this phase, the WWO algorithm is introduced to optimize the parameters. The algorithm has powerfully global and local searching capabilities and avoids falling into local optimum while ensuring the optimization accuracy. Therefore, the WWO algorithm could ensure the reliability of scattering center parameter estimation. The single scattering center after solution is eliminated from the original image and the residual image is segmented into high-energy regions, so the parameters of the next scattering center are estimated sequentially. Finally, the parameter set of all scattering centers in the input SAR image can be obtained. In the experiments, firstly, the parameter estimation verification is performed based on the SAR images in the MSTAR dataset. The comparison of the parameter estimation results with the original image and the reconstruction based on the estimated parameter set reflect the effectiveness of the proposed method. In addition, the experiment is also conducted using the SAR target recognition algorithms based on the estimated attribute parameters. By comparing the recognition performance with other parameter estimation algorithms under the same conditions, the performance superiority of the proposed method in attribute scattering center parameter estimation is further demonstrated.

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Full-Polarization Scattering Center Extraction and Parameter Estimation: P-ESPRIT Algorithm

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2006.02083 ◽

2011 ◽

Vol 30 (8) ◽

pp. 1963-1967

Author(s):

Da-hai Dai ◽

Xue-song Wang ◽

Shi-qi Xing ◽

Shun-ping Xiao

Keyword(s):

Parameter Estimation ◽

Scattering Center ◽

Esprit Algorithm

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Rotating Parabolic-Reflector Antenna Target in SAR Data: Model, Characteristics, and Parameter Estimation

International Journal of Antennas and Propagation ◽

10.1155/2013/583865 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Bin Deng ◽

Hong-Qiang Wang ◽

Yu-Liang Qin ◽

Sha Zhu ◽

Xiang Li

Keyword(s):

Parameter Estimation ◽

Data Model ◽

Target Recognition ◽

Image Interpretation ◽

Likelihood Estimator ◽

Parabolic Reflector ◽

Time Frequency ◽

Aspect Angle ◽

Multiple Parameters ◽

Scattering Center

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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