Forward Scattering by Coated Objects Illuminated by Short Wavelength Radar

Hiatt, Ralph E.; Siegel, Keeve Milton; Weil, Herschel

doi:10.1109/jrproc.1960.287679

Cited by 26 publications

(12 citation statements)

References 4 publications

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“…(2) Signal scattered by a target corresponds to the forward scattering with the maximum of the shadow radiation along the Tx-Rx, when the target in on the baseline (3) Being 'shadow' the forward scatter signal is π/2 phase shifted (imaginary along the FS axis) relative to the direct path signal [13,15].…”

Section: Doppler Signature At the Output Of Rssi Receivermentioning

confidence: 99%

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Phenomenology of signals in FSR for surface targets detection

Gashinova

Daniel

Kabakchiev

et al. 2012

IET International Conference on Radar Systems (Radar 2012)

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In this paper the Doppler phase signature forming in Forward Scatter Radar (FSR) is analyzed to show the rationale for "true" forward scatter radar where a target actually crosses the baseline and the whole Doppler signature is used for target detection, speed and trajectory estimation. Several measured Doppler signatures are compared where the target is in one of diffraction zones with the view to separate between pure shadow radiation and bistatic scattering. Two identical objects, metallic and magnetic absorber covered, are chosen to demonstrate the difference in shadow and bistatic scattering mechanisms.

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Section: Doppler Signature At the Output Of Rssi Receivermentioning

confidence: 99%

“…Analytical solutions for the FS CS are only available for few convex shapes using PO and Physical Theory of Diffraction approaches [15]. Thus either approximated models or 3D EM simulation methods must be used for target FS CS estimation.…”

Section: Forward Scatter Cross Section Approximationmentioning

confidence: 99%

Phenomenology of signals in FSR for surface targets detection

Gashinova

Daniel

Kabakchiev

et al. 2012

IET International Conference on Radar Systems (Radar 2012)

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“…They do not work against grazing incident waves. That is why ordinary RAM's do not reduce forward scattering [157]. The idea to find a nonreflecting absorbing layer (homogeneous in the direction parallel to the reflecting metallic plate) for gazing incidence is tempting but unfortunately not feasible [ 1581.…”

Section: Reduction Of Monostatic Rcs By Body Shapingmentioning

confidence: 99%

Comments on diffraction principles and limitations of RCS reduction techniques

Ufimtsev

1996

Proc. IEEE

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Reduction of radar cross sections (RCS) is &ne of the major problems in stealth technology. Basic techniques used for RCS reduction are well known. They involve appropriate shaping, use of radar absorbing materials (RAM'S), and passive and active cancellation of the scattered jield. Detailed description of these techniques and related bibliography have been presented in RCS handbooks. In this paper we discuss these techniques briefly from the physical point of view and summarize their limitations. Attention is concentrated on discussions of the physical structure of radar waves scattered from large objects (compared with the radar wavelength). These waves consist of ordinary and diffracted rays, thier beams, and shadow radiation. The latter is caused by the transverse diffusion of the radar beam in the vicinity of the shadow boundaries behind the scattering object, i.e., in the penumbra region. Shadow radiation creates strong forward scattering and is responsible for RCS enhancement at large bistatic angles. Possible passive and active techniques to control and reduce reflected beams, rays, and shadow radiation and potential limitations of these techniques are considered in the paper. In particular, it is emphasized that grazing rejlected rays and shadow radiation cannot be eliminated by absorbing materials. 1830

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“…Its' most attractive feature is the steep rise in the target Radar Cross Section (RCS) compared to traditional monostatic radar [3][4][5][6] , which improves the sensitivity of the radar system. The forward scattering RCS mainly depends on the target's physical cross section and the wavelength and is independent of the target's surface shape as well as to any Radar Absorbing Material (RAM) coating which reduces the target's RCS in traditional radar [7] . This feature makes FSR robust to stealth technology.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Approach in Radar Target Classification

Ibrahim¹

2009

Journal of Computer Science

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Problem statement: This study unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR). In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN). Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP) backpropagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.

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Forward Scattering by Coated Objects Illuminated by Short Wavelength Radar

Cited by 26 publications

References 4 publications

Phenomenology of signals in FSR for surface targets detection

Phenomenology of signals in FSR for surface targets detection

Comments on diffraction principles and limitations of RCS reduction techniques

Artificial Neural Network Approach in Radar Target Classification

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