Bayesian framework for detector development in Pareto distributed clutter

Weinberg, Graham V.; Howard, Stephen D.; Tran, Charlie

doi:10.1049/iet-rsn.2018.5635

Cited by 16 publications

(4 citation statements)

References 26 publications

(52 reference statements)

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“…For instance, logt-CFAR, Maximum Likelihood (ML) CFAR, [zlog(z)]-CFAR, Trimmed Mean Order Statistic (TMOS) CFAR and Robust Variability Index (RVI) CFAR detectors are proposed in presence of Weibull and log-normal disturbances [9]. For the case of Pareto type I and Pareto type II clutter models, Geometric Mean (GM) CFAR and Bayesian-CFAR procedures are proposed by Weinberg et al [10,11]. Fully CFAR properties are maintained for any values of scale and shape parameters.…”

Section: Statistical Properties Of This Type Of Clutter Have Been App...mentioning

confidence: 99%

OSTM-CFAR Detector in Heterogeneous Pareto type II Radar Clutter

Zebiri,

Mezache

2024

Preprint

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In this paper, we propose a non-coherent CFAR decision rule named OSTM-CFAR (Order Statistic Trimmed Mean Constant False Alarm rate) detector in a Pareto type II clutter with unknown scale and shape parameters. This is obtained after adequate selection of two scale invariant functions in the general hypothesis test. The resulting test statistic provides rapid calculation because is independent of any estimation method or numerical integration. Through simulated and real data, CFAR properties of suggested CFAR detector are maintained against both scale and shape parameters of clutter model. Besides, detection probability of both OSTM-CFAR and Bayesian-CFAR procedures are sketched together where the proposed one demonstrates its superiority in presence of interfering targets.

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Section: Statistical Properties Of This Type Of Clutter Have Been App...mentioning

confidence: 99%

OSTM-CFAR Detector in Heterogeneous Pareto type II Radar Clutter

Zebiri,

Mezache

2024

Preprint

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“…In terms of sea clutter model types, several authors have considered different decision rules that are independent of true clutter parameters [17][18][19][20]. Several CFAR detectors operating in homogeneous and heterogeneous Pareto type II clutter are derived when the shape parameter or the scale parameter of the Pareto type II model is known a priori [19].…”

Section: Introductionmentioning

confidence: 99%

“…Several CFAR detectors operating in homogeneous and heterogeneous Pareto type II clutter are derived when the shape parameter or the scale parameter of the Pareto type II model is known a priori [19]. If these parameters are unknown, an alternative procedure based upon the Bayesian approach is introduced in which a modified decision rule is given in integral form [20].…”

Section: Introductionmentioning

confidence: 99%

Multi-headed deep learning-based estimator for correlated-SIRV Pareto type II distributed clutter

Kerbaa

Mezache

Gini

et al. 2023

EURASIP J. Adv. Signal Process.

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This paper deals with the problem of estimating the parameters of heavy-tailed sea clutter in high-resolution radar, when the clutter is modeled by the correlated Pareto type II distribution. Existing estimators based on the maximum likelihood (ML) approach, integer-order moments (IOM) approach, fractional-order moments (FOM), and log-moments (log-MoM) have shown to be sensitive to changes in data correlation. In this work, we resort to a deep learning (DL) approach based on a multi-headed architecture to overcome this problem. Offline training of the artificial neural networks (ANN) is carried out by using several combinations of the clutter parameters, with different correlation degrees. To assess the performance of the proposed estimator, we resort to Monte Carlo simulation, and we observed that it has superior performance over existing approaches in terms of estimation mean square error (MSE) and robustness to changes of the clutter correlation coefficient.

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“…Popular K distribution is widely applied in many disciplines of radar signal processing and is obtained from a gamma distributed texture component. The well-known Pareto type II model has been shown to occur as intensity distribution of the compound Gaussian process with an inverse gamma texture [3]. The compound Gaussian inverse Gaussian (CGIG) distribution is constructed if the modulation component follows the inverse Gaussian law [4].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Parameter Estimation of Radar Sea-Clutter with Trimodal Gamma Population

Terki¹,

Mezache²,

Chebbara³

2022

JTIT

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Real radar data often consist of a mixture of Gaussian and non-Gaussian clutter. Such a situation creates one or more inflexion points in the curve of the empirical cumulative distributed function (CDF). In order to obtain an accurate fit with sea reverberation data, we propose, in this paper, a trimodal gamma disturbance model and two parameter estimators. The non-linear least-squares (NLS) fit approach is used to avoid computational issues associated with the maximum likelihood estimator (MLE) and moments-based estimator for parameters of the mixture model. For this purpose, a combination of moment fit and complementary CDF (CCDF) NLS fit methods is proposed. The simplex minimization algorithm is used to simultaneously obtain all parameters of the model. In the case of a single gamma probability density function, a zlog(z) method is derived. Firstly, simulated life tests based on a gamma population with different shape parameter values are worked out. Then, numerical illustrations show that both MLE and zlog(z) methods produce closer results. The proposed trimodal gamma distribution with moments NLS fit and CCDF NLS fit estimators is validated to be in qualitative agreement with different cell resolutions of the available IPIX database.

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Bayesian framework for detector development in Pareto distributed clutter

Cited by 16 publications

References 26 publications

OSTM-CFAR Detector in Heterogeneous Pareto type II Radar Clutter

OSTM-CFAR Detector in Heterogeneous Pareto type II Radar Clutter

Multi-headed deep learning-based estimator for correlated-SIRV Pareto type II distributed clutter

Modeling and Parameter Estimation of Radar Sea-Clutter with Trimodal Gamma Population

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