Comparison of bi‐modal coherent sea clutter simulation techniques

Rosenberg, Luke; Bocquet, Stephen

doi:10.1049/iet-rsn.2018.5627

Cited by 9 publications

(12 citation statements)

References 27 publications

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“…The data has been analysed for different look directions in azimuth, but the results presented here are confined to the upwind look direction. Results for other azimuth directions are shown in [20]. The wind and swell directions coincide, so upwind is also up swell.…”

Section: Ingara Datamentioning

confidence: 99%

“…The method is demonstrated with a simulation for HH polarisation looking upwind. Simulation examples for VV polarisation and other look directions are presented in [20]. Range‐Doppler images of the data and simulation are shown in Fig.…”

Section: Simulation Of Clutter Spectramentioning

confidence: 99%

“…Recently, McDonald and Cerutti‐Maori (MCM) [17, 18] proposed a two‐component model where the spatial variability in the Bragg component is derived from a regular wave pattern, but there is no Doppler–intensity correlation. Their model is compared to data from the DST Group Ingara radar in [19, 20].…”

Section: Introductionmentioning

confidence: 99%

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Compound G2 model of coherent radar sea clutter

Bocquet

2019

IET Radar, Sonar & Navigation

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Section: Ingara Datamentioning

confidence: 99%

Section: Simulation Of Clutter Spectramentioning

confidence: 99%

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Compound G2 model of coherent radar sea clutter

Bocquet

2019

IET Radar, Sonar & Navigation

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“…If we assume that p does not change much relative to β, which is a reasonable assumption due to the fact that radar antennas usually have a very small azimuthal beamwidth, we can express p in terms of a "baseband" Doppler spectrum, i.e., the Doppler spectrum caused only by ICM that would be seen by a stationary radar with an infinitesimal beamwidth. The ICM spectrum is usually what is discussed in papers on Doppler spectrum modeling (e.g., [28,29,53]). We will denote this quantity as b(ρ|τ) and note that under the narrow beamwidth assumption, p(ρ|β, τ) ≈ b(ρ − β|τ), therefore:…”

Section: Side-looking Antenna Level Flight Pathmentioning

confidence: 99%

“…Specifically, the range-Doppler scattering function can be used to predict pulse-to-pulse correlations in the clutter return to suppress it using eigenfilter techniques [27,28]. An up-to-date summary of recent work on Doppler spectrum modeling can be found in [29]. Most papers on Doppler spectrum modeling do not use Bello's LTV formulation; the effect of the transmit waveform, measurement system, and clutter are usually combined.…”

Section: Introductionmentioning

confidence: 99%

Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions

Cooke¹

2019

Entropy

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Bello’s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth’s surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a frequency-selective generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

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Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

Liang

Shui

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In high-resolution maritime surveillance radars, sea clutter exhibits highly spatial heterogeneity due to modulation of long waves with wavelengths longer than the width of one range cell. Compound-Gaussian model (CGM) fails to characterize the heterogeneous high-resolution sea clutter in both amplitude distribution and Doppler spectrum. In this paper, a bi-phase compound-Gaussian mixture model (BP-CGMM) is proposed to characterize the heterogeneous sea clutter. In the BP-CGMM, spatial resolution cells are grouped into two disjoint sets, and the sea clutter in each set is represented by one CGM with inverse Gamma-distributed texture. The spectral heterogeneity indicates that sea clutter vectors at spatially adjacent resolution cells in one set share the same speckle covariance matrix, while that at two adjacent spatial cells separated in the two sets often have different speckle covariance matrices. The BP-CGMM is validated by a mass of measured high-resolution sea clutter data. Moreover, under the BP-CGMM, a detection method based on batch test is given to detect sea-surface small targets, which is composed of scene segmentation, by the aid of Bayesian threshold and morphological filtering, and adaptive generalized likelihood ratio test linear-threshold detector (GLRT-LTD) separately in each set. The detection method is verified by measured data with small targets under test. The experimental results show that it attains better detection performance than the adaptive GLRT-LTD under the CGM of sea clutter. Index Terms-Modulation of long waves; Heterogeneous high-resolution sea clutter; Compound-Gaussian mixture model; Spectral heterogeneity; Scene segmentation.

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Comparison of bi‐modal coherent sea clutter simulation techniques

Cited by 9 publications

References 27 publications

Compound G2 model of coherent radar sea clutter

Compound G2 model of coherent radar sea clutter

Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

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