Land clutter models for radar design and analysis

Barton, David K.

doi:10.1109/proc.1985.13133

Cited by 52 publications

(16 citation statements)

References 2 publications

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“…-31.4 dB). At X band r o for grass with low grazing angle is about -30 dB [7][8][9], which is in fairly good agreement with the theory. Figure 5 shows an overlay between the Google Earth map with the RCS map in the suburban environment of Tor Vergata area.…”

Section: Rcs Estimation Of Corner Reflectors Spheres Fixed Target Asupporting

confidence: 68%

Generation of Land-Clutter Maps for Cognitive Radar Technology

Galati

Pavan

2018

Advances in Intelligent Systems and Computing

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Abstract. The concept of cognitive radar is based on an intelligent signal processing obtained by the interaction of the radar with the surrounding environment in order to optimize the radar operation. In the frame of the vast literature on measurement and modelling of the echoes from the environment such as land, vegetation, man-made infrastructures (called land clutter or fixed clutter in the radar jargon), this paper is aimed to contribute with a practical methodology based on a moveable, lightweight, small and cheap marine radar and its calibration. Live results, related to Radar Cross Section measurements in a typical suburban area, are also shown and analysed. Buildings, streets, highways, car parking areas with different coatings, large and medium size lampposts, soil with grass, trees, all in a radius of a very few kilometres, makes this area interesting as far as land clutter characterization is concerned.

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Section: Rcs Estimation Of Corner Reflectors Spheres Fixed Target Asupporting

confidence: 68%

Generation of Land-Clutter Maps for Cognitive Radar Technology

Galati

Pavan

2018

Advances in Intelligent Systems and Computing

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“…1), and concentrates on low grazing angles. Following Barton's approach to land clutter [15], in-plane geometries are modelled as the geometric mean of the monostatic clutter from the transmitter and receiver positions. For out-of-plane clutter, this method is extended to include the effects of changing the look angle, skewdepolarisation, cross-polarised returns and the scattering pattern of the clutter patch.…”

Section: Empirical Modellingmentioning

confidence: 99%

“…Willis [7] analysed the Domville data, using Barton's [15] model for bistatic land clutter, which is of the form…”

Section: Modelling In-plane Cluttermentioning

confidence: 99%

Measurement and modelling of bistatic radar sea clutter

Griffiths

Al-Ashwal

Ward³

et al. 2010

IET Radar Sonar Navig.

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Bistatic radar is a subject of considerable present interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at best. The purpose of this study is to present an analysis of the limited existing published radar data and to derive an empirical model, which expresses the variation of mean s 0 with the measurement geometry and sea conditions. This empirical model is then compared with electromagnetic (EM) scattering calculations using the composite model (sometimes called the two-scale model) to show the extent to which the EM model is able to reproduce the trends observed in the data. The results indicate where improvements to bistatic EM modelling are required (very low grazing angles and out-of-plane scattering), and a general need for more radar data to extend the empirical s 0 model and expand it to the bistatic clutter statistics.

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“…The RCS of the clutter in the q th patch is equal to the product of clutter reflectivity and patch area. Assuming the constantgamma model, clutter reflectivity is equal to γsinψ q , where γ is a terrain dependent parameter, and ψ q is the grazing angle for the q th patch [7]. For the homogeneous clutter environment, data were generated with a constant gamma of -10 dB.…”

Section: A Homogeneous Distributed Cluttermentioning

confidence: 99%

Performance and computational trades for RD-STAP algorithms in challenging detection environments

Paulus

Melvin

Himed

2016

2016 IEEE Radar Conference (RadarConf)

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Reducing the size, weight, and power (SWAP) of airborne radar systems improves efficiency and decreases operational cost. Platform SWAP requirements are directly related to the complexity of algorithms used to process the received signal. Several reduced dimension space-time adaptive processing (RD-STAP) algorithms, which significantly decrease computations over traditional STAP, have been shown to minimally impact GMTI performance in homogeneous clutter. In both heterogeneous clutter and dense target environments, training with data whose characteristics match those of the cell under test may be required to maintain acceptable performance; however, selecting appropriate training data requires additional computations that increase SWAP. This paper identifies and quantifies trades between performance and computations for several RD-STAP algorithms and training methods in challenging signal environments.

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Land clutter models for radar design and analysis

Cited by 52 publications

References 2 publications

Generation of Land-Clutter Maps for Cognitive Radar Technology

Generation of Land-Clutter Maps for Cognitive Radar Technology

Measurement and modelling of bistatic radar sea clutter

Performance and computational trades for RD-STAP algorithms in challenging detection environments

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