Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture

Balleri, Alessio; Nehorai, Arye; Wang, Jian

doi:10.1109/taes.2007.4285370

Cited by 199 publications

(135 citation statements)

References 10 publications

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“…Another Pearson diffusion with volatility = ax 2 yields a texture that has an inverse -distribution with scale parameter a/m and shape parameter 1 + 1/a. The resulting distribution for the envelope was shown to match well data from a lake clutter [1]. In a slightly different context (synthetic aperture radar interferometry), two additional Pearson diffusions were successfully confronted with real data [4] Apart from these four instances of (2.9), other Pearson diffusions may prove adequate for scattering applications (whether the resulting distribution for the intensity is tractable or not).…”

Section: Pearson Diffusionsmentioning

confidence: 92%

“…We have thus encompassed the wider class of intensity distributions considered in [1] and [4] for which we have derived their autocorrelation and spectral properties.…”

Section: P Fayard and T R Fieldmentioning

confidence: 99%

“…More precisely, we can derive a stochastic differential equation (SDE) for the scattering radar cross section (RCS), defined as the continuous limit of such a population for an asymptotically large number of individual scatterers, whose asymptotic -distribution, the socalled texture, gives rise to the K-distribution for the scattered intensity [8]. Radar engineers have postulated various other distribution models for this texture [1], [4]. Motivated by the mathematical basis of the -texture provided by the BDI process, the present paper succeeds in deriving the other texture models considered in the literature on the basis of an underlying discrete population.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Models for Scattering Populations

Fayard

Field

2011

J. Appl. Probab.

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Jakeman's random walk model with step number fluctuations describes the coherent amplitude scattered from a rough medium in terms of the summation of individual scatterers' contributions. If the scattering population conforms to a birth-death immigration model, the resulting amplitude is K-distributed. In this context, we derive a class of diffusion processes as an extension of the ordinary birth-death immigration model. We show how this class encompasses four different cross-section models commonly studied in the literature. We conclude by discussing the advantages of this unified description.

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Section: Pearson Diffusionsmentioning

confidence: 92%

“…We have thus encompassed the wider class of intensity distributions considered in [1] and [4] for which we have derived their autocorrelation and spectral properties.…”

Section: P Fayard and T R Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete Models for Scattering Populations

Fayard

Field

2011

J. Appl. Probab.

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“…In X-band maritime surveillance radar, the Pareto distribution has become of much interest as a clutter intensity model due to its validation relative to real radar clutter returns [16][17][18]. This model arises as the intensity distribution of a compound Gaussian model with inverse Gamma texture.…”

Section: Mappingmentioning

confidence: 99%

Nonlinear Transformations and Radar Detector Design

Weinberg¹

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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A nonlinear transformation is introduced, which can be used to compress a series of random variables. For a certain class of random variables, the compression results in the removal of unknown distributional parameters from the resultant series. Hence, the application of this transformation is investigated from a radar target detection perspective. It will be shown that it is possible to achieve the constant false alarm rate property through a simple manipulation of this transformation. Due to the effect the transformation has on the cell under test, it is necessary to couple the approach with binary integration to achieve reasonable results. This is demonstrated in an X-band maritime surveillance radar detection context.

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“…In this work, we model the clutter as a compound-Gaussian process with inverse gamma texture pdf [16][17][18] and derive the corresponding ML estimator of the covariance matrix. To compare this estimator to NSCM and AML, we use it in the ANMF test and analyze its performance in terms of false alarm (P f a ) and detection (P d ) probabilities as well as computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Performance of the Adaptive Normalized Matched Filter Detector in Compound-Gaussian Clutter With Inverse Gamma Texture Model

Younsi¹,

Nadhor²

2011

PIER B

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Abstract-In the present paper, we deal with the performance analysis of the Adaptive Normalized Matched Filter (ANMF) detector in compound-Gaussian clutter with inverse gamma texture model and unknown covariance matrix. First, the maximum likelihood estimate (MLE) of the covariance matrix for this clutter model is derived. The MLE is then plugged into the ANMF test and compared to the well known normalized sample covariance matrix estimate (NSCM) and the approximate maximum likelihood estimate (AML). The performance in terms of CFAR behavior and detection probability is evaluated in the presence of simulated clutter and real sea clutter data, which is collected by the McMaster IPIX radar.

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Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture

Cited by 199 publications

References 10 publications

Discrete Models for Scattering Populations

Discrete Models for Scattering Populations

Nonlinear Transformations and Radar Detector Design

Performance of the Adaptive Normalized Matched Filter Detector in Compound-Gaussian Clutter With Inverse Gamma Texture Model

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