Simulation of coherent sea clutter

Davidson, G.

doi:10.1049/iet-rsn.2009.0075

Cited by 33 publications

(19 citation statements)

References 8 publications

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“…Since a larger value of the normalized second intensity moment represents a spikier nature [1], it can be inferred that the simulated sea clutter at the tails of the Doppler spectrum are spikier than the sea clutter located at the bulk. This is similar with the phenomenon observed in the realistic sea clutter [1], [3].…”

Section: B Simulation Results Of Long-term Sea Cluttersupporting

confidence: 89%

“…For sea clutter over a short time scale, the Doppler spectrum over the whole measurement is used to describe its temporal correlation characteristics. However, over a long time scale, measurement data show that the short-term Doppler spectrum changes with time [1], [3], [4], [24]- [27]. The time-varying Doppler spectrum is due to the nonstationarity of the sea clutter and presents the second-order nonstationarity of the sea clutter series.…”

Section: Introductionmentioning

confidence: 99%

“…However, the model is based on the average Doppler spectrum and does not involve the temporal variation of the short-term Doppler spectrum series. Davidson [3] developed a flexible method to directly simulate the desired Doppler spectra instead of generating the underlying clutter at first. Although the time series of the simulated Doppler spectrum is coherent, the underlying sea clutter returns between different blocks are not coherent.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Wang

2015

IEEE Trans. Geosci. Remote Sensing

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It is known that the major difficulty of sea clutter simulation is the controlled generation of a continuous correlated non-Gaussian random process. In particular, for sea clutter at low grazing angles and over a long time scale, the spiky and nonstationary nature makes the simulation more difficult. This paper proposes a novel procedure for the simulation of continuous correlated low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. The Pareto distribution is utilized to describe the statistics of the sea clutter intensity. In addition, different correlation characteristics for sea clutter over both short and long time scales are considered in this paper. First, the memoryless nonlinear transform is adopted to simulate the intensities of the correlated sea clutter. Second, the Doppler spectra of different range bins are generated separately. In particular, for the sea clutter over long time scales, successive time-varying Doppler spectra are modeled to characterize the nonstationarity of the sea clutter series. Then, the phases of the sea clutter are retrieved by constraining to the desired Doppler spectra and given magnitudes. Different methods are adopted for the phase retrieval of sea clutter on short and long time scales. Finally, simulation results show that the proposed procedure can generate continuous low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. In particular, for the sea clutter over a long time scale, the simulated sea clutter series enjoys the time-varying Doppler spectrum while maintaining continuity. In addition, the proposed method is also suitable for other models, such as the K-distribution, the Weibull model, and so on.

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Section: B Simulation Results Of Long-term Sea Cluttersupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Wang

2015

IEEE Trans. Geosci. Remote Sensing

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“…The red, high power, band from 0m/s to -4m/s represents the clutter as predicted by a multiple Gaussian Doppler, compound K power distribution e.g. Davidson [11]. However a much lower power, yellow band is clearly seen, and well defined, between -11 to -15m/s.…”

Section: Introductionmentioning

confidence: 97%

Radar clutter and propagation observations by UK ARTIST in 2010

Wallace

2013

2013 IEEE Radar Conference (RadarCon13)

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A set of radar trials, looking at advanced multifunction radar (MFR) technology, were undertaken, in a United Kingdom (UK) and United States (US) collaboration, at Wallops Island, Virginia, USA, throughout the year 2010. During the trials, a wealth of high quality radar and meteorological data were recorded under a wide range of clutter, weather and propagation conditions. A small selection of this data has been analysed and the clutter returns compared to a set of standard models. Observations of note included; the presence of strong anomalous propagation, a high Doppler component to sea clutter and the tracking of 'angels' occupying a target kinematic space similar to that of unmanned air vehicles (UAVs).

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“…Various models for sea clutter have been proposed with simulation techniques [2]. Traditionally, sea clutter is commonly described as a non-Gaussian stochastic process [3], such as Weibull, log-normal, K, and compound Gaussian distributions [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Sea clutter constituent synthesis approach based on a new decomposition model

Zhang

et al. 2015

EURASIP J. Adv. Signal Process.

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In this paper, a sea clutter decomposition model is newlxy proposed. The decomposition structure is organized according to a comparison study between measured sea clutter and Lorenz chaotic signals. Based on the decomposition model, a sea clutter constituent synthesis approach is developed to reconstruct sea clutter series with neural networks. Simulation results demonstrate the effectiveness and stability of the proposed approach.

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Simulation of coherent sea clutter

Cited by 33 publications

References 8 publications

Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Radar clutter and propagation observations by UK ARTIST in 2010

Sea clutter constituent synthesis approach based on a new decomposition model

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