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

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
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.

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

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

scholarly journals Wavelet multi-resolution approximation of time-varying frame structure

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879559 ◽  
Author(s):  
Min Xiang ◽  
Feng Xiong ◽  
Yuanfeng Shi ◽  
Kaoshan Dai ◽  
Zhibin Ding
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Identification Problem ◽  
Frame Structure ◽  
Structural Parameter ◽  
Time Varying ◽  
Engineering Structures ◽  
Time Frequency ◽  
Non Parametric

Engineering structures usually exhibit time-varying behavior when subjected to strong excitation or due to material deterioration. This behavior is one of the key properties affecting the structural performance. Hence, reasonable description and timely tracking of time-varying characteristics of engineering structures are necessary for their safety assessment and life-cycle management. Due to its powerful ability of approximating functions in the time–frequency domain, wavelet multi-resolution approximation has been widely applied in the field of parameter estimation. Considering that the damage levels of beams and columns are usually different, identification of time-varying structural parameters of frame structure under seismic excitation using wavelet multi-resolution approximation is studied in this article. A time-varying dynamical model including both the translational and rotational degrees of freedom is established so as to estimate the stiffness coefficients of beams and columns separately. By decomposing each time-varying structural parameter using one wavelet multi-resolution approximation, the time-varying parametric identification problem is transformed into a time-invariant non-parametric one. In solving the high number of regressors in the non-parametric regression program, the modified orthogonal forward regression algorithm is proposed for significant term selection and parameter estimation. This work is demonstrated through numerical examples which consider both gradual variation and abrupt changes in the structural parameters.

scholarly journals Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1679 ◽  
Author(s):  
Ronghui Zhan ◽  
Liping Wang ◽  
Jun Zhang
Keyword(s):  
Sequential Monte Carlo ◽  
Multiple Targets ◽  
Range Resolution ◽  
Aspect Angle ◽  
Scattering Center ◽  
Simulation Results ◽  
Wideband Radar ◽  
Joint Tracking ◽  
High Range

This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP) with the information of the target aspect angle, and target classification is implemented through the data correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-target JTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTC method into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter with Bayesian theory. To further tackle the complex integrals’ calculation involved in targets state and class estimation, we finally provide the sequential Monte Carlo (SMC) implementation of the proposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC method is validated by simulation results under the application scenario of maritime ship surveillance.

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