Topic

# Radar cross-section

About: Radar cross-section is a research topic. Over the lifetime, 5082 publications have been published within this topic receiving 60898 citations. The topic is also known as: RCS & radar cross section.

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TL;DR: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations, which is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.

Abstract: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory's (JPLs) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.

1,838 citations

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TL;DR: It is shown that MIMO radar leads to significant performance improvement in DF accuracy, and is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction.

Abstract: It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.

1,437 citations

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New York University

^{1}, New Jersey Institute of Technology^{2}, Lehigh University^{3}, University of Delaware^{4}, Alcatel-Lucent^{5}TL;DR: The optimal detector in the Neyman–Pearson sense is developed and analyzed for the statistical MIMO radar and it is shown that the optimal detector consists of noncoherent processing of the receiver sensors' outputs and that for cases of practical interest, detection performance is superior to that obtained through coherent processing.

Abstract: Inspired by recent advances in multiple-input multiple-output (MIMO) communications, this proposal introduces the statistical MIMO radar concept To the authors' knowledge, this is the first time that the statistical MIMO is being proposed for radar The fundamental difference between statistical MIMO and other radar array systems is that the latter seek to maximize the coherent processing gain, while statistical MIMO radar capitalizes on the diversity of target scattering to improve radar performance Coherent processing is made possible by highly correlated signals at the receiver array, whereas in statistical MIMO radar, the signals received by the array elements are uncorrelated Radar targets generally consist of many small elemental scatterers that are fused by the radar waveform and the processing at the receiver, to result in echoes with fluctuating amplitude and phase It is well known that in conventional radar, slow fluctuations of the target radar cross section (RCS) result in target fades that degrade radar performance By spacing the antenna elements at the transmitter and at the receiver such that the target angular spread is manifested, the MIMO radar can exploit the spatial diversity of target scatterers opening the way to a variety of new techniques that can improve radar performance This paper focuses on the application of the target spatial diversity to improve detection performance The optimal detector in the Neyman–Pearson sense is developed and analyzed for the statistical MIMO radar It is shown that the optimal detector consists of noncoherent processing of the receiver sensors' outputs and that for cases of practical interest, detection performance is superior to that obtained through coherent processing An optimal detector invariant to the signal and noise levels is also developed and analyzed In this case as well, statistical MIMO radar provides great improvements over other types of array radars

1,271 citations

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28 Feb 1993

TL;DR: In this paper, the authors show how the RCS gauge can be predicted for theoretical objects and how it can be measured for real targets, and the two most practical ways to reduce RCS are shaping and absorption.

Abstract: Radar cross section (RCS) is a comparison of two radar signal strengths. One is the strength of the radar beam sweeping over a target, the other is the strength of the reflected echo sensed by the receiver. This book shows how the RCS gauge can be predicted for theoretical objects and how it can be measured for real targets. Predicting RCS is not easy, even for simple objects like spheres or cylinders, but this book explains the two exact forms of theory so well that even a novice will understand enough to make close predictions. Weapons systems developers are keenly interested in reducing the RCS of their platforms. The two most practical ways to reduce RCS are shaping and absorption. This book explains both in great detail, especially in the design, evaluation, and selection of radar absorbers. There is also great detail on the design and employment of indoor and outdoor test ranges for scale models or for full-scale targets (such as aircraft). In essence, this book covers everything you need to know about RCS, from what it is, how to predict and measure, and how to test targets (indoors and out), and how to beat it.

1,050 citations

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Avco

^{1}TL;DR: In this paper, a new method is proposed for the computation of the radar cross section and other associated field quantities arising when a smooth, perfectly conducting obstacle is illuminated by an incident electromagnetic wave.

Abstract: A new method is proposed for the computation of the radar cross section and other associated field quantities arising when a smooth, perfectly conducting obstacle is illuminated by an incident electromagnetic wave. The scattered wave is first represented by a distribution of electric dipoles over the surface in question, with the response from any dipole proportional to the induced surface current density at that point. The surface current is then determined by the "boundary condition" that the scattered wave, through interference, precisely cancels the incident wave inside the obstacle. One obtains in this mariner a pair of coupled (infinite) matrix equations for the surface current. Green's identity permits decoupling of the equations, reducing the problem to roughly the equivalent of two independent scalar problems. The equations have been specialized to axially symmetric obstacles and then solved numerically on the IBM 7094 for several examples of interest. Reciprocity and energy conservation are also examined and the resonant mode (interior) problem set up explicitly in matrix form.

903 citations