Utilization of aspect angle information in synthetic aperture images

2018 ◽  
Vol 143 (3) ◽  
pp. 1855-1855
Author(s):  
Daniel Plotnick ◽  
Timothy Marston
2018 ◽  
Vol 56 (9) ◽  
pp. 5424-5432 ◽  
Author(s):  
Daniel S. Plotnick ◽  
Timothy M. Marston

2021 ◽  
Vol 10 (1) ◽  
pp. 127-134
Author(s):  
Anton V. Kvasnov ◽  
Vyacheslav P. Shkodyrev

Abstract. The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.


2021 ◽  
Vol 35 (11) ◽  
pp. 1358-1359
Author(s):  
Aaron Brandewie ◽  
Robert Burkholder

Objects in low earth orbit such as CubeSats and the International Space Station (ISS) move with constant velocity along a linear trajectory when viewed from a ground-based radar. The small change in attitude of the object as it flies overhead permits the generation of an inverse synthetic aperture radar (ISAR) image. In this paper, Altair’s FEKO™ software is used to model the monostatic radar scattering from the ISS as a function of frequency and aspect angle. The computed data is used for generating a simulated ISAR image from a ground-based radar. The system design requirements for the radar are calculated from the radar equation.


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