M. GOURI Amel

MCB

Directory of teachers

Department

BASE COMMON ST Departement ST

Research Interests

Specialized in BASE COMMON ST Departement ST. Focused on academic and scientific development.

Contact Info

University of M'Sila, Algeria

Email : gouri.amel@univ-msila.dz

On the Web:

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Recent Publications

2023-12-27

statistical Analysis of Sea-Clutter using K-Pareto, K-CGIG, and Pareto-CGIG Combination Models with Noise

In this paper, the combinations of two compound Gaussian distributions plus thermal noise for modeling measured polarimetric clutter data are proposed. The speckle components of the proposed models are formed by the exponential distribution, while the texture components are mainly modeled using three different distributions. For this purpose, the gamma, the inverse gamma, and the inverse Gaussian distributions are considered to describe these modulation components. The study involves the analysis of underlying mixture models at X-band sea clutter data, and the parameters of the combination models are estimated using the non-linear least squares curve fitting method. Compared to existing K, Pareto type II, and KK clutter plus noise distributions, experimental results show that the proposed mixture models are well matched for fitting sea reverberation data across various range resolutions.
Citation

M. GOURI Amel, (2023-12-27), "statistical Analysis of Sea-Clutter using K-Pareto, K-CGIG, and Pareto-CGIG Combination Models with Noise", [national] WSEAS TRANSACTIONS on SIGNAL PROCESSING , WSEAS

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2020-03-20

Radar CFAR detection in Weibull clutter based on zlog(z) estimato

In this paper, the zlog(z) based estimator for constant false alarm rate (CFAR) detection in Weibull clutter is proposed. This estimation method is obtained in terms of the digamma function where the estimates of the shape parameter are determined by the interpolation tool. The non-integer order moments estimator (NIOME) is also given and coincides the zlog(z) estimation results for low values of the moment’s fractional order. Via simulated data, it is shown that the CFAR detection performances based on the zlog(z) estimator have almost similar results as well as the existing maximum likelihood (ML) CFAR detector, but with low time-consuming which is very important in real-time applications
Citation

M. GOURI Amel, (2020-03-20), "Radar CFAR detection in Weibull clutter based on zlog(z) estimato", [national] Remote Sensing Letters , Taylor&Francis

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2019-04-26

Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Biogeography Based Optimization (BBO) method is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the open literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection
Citation

M. GOURI Amel, (2019-04-26), "Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool", [national] International Journal of Information Science & Technology (IJIST) , International Journal of Information Science & Technology

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2019-02-09

Optimization of Distributed CFAR Detection using Grey Wolf Algorithm

In this paper, decentralized constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Grey Wolf Optimization technique is investigated. For independent signals with known power, optimal thresholds of local Greatest Of-CFAR and Smallest Of-CFAR detectors are optimized simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, both the Biogeography Based Optimization and the Grey Wolf Optimization tools are used to conduct distributed CFAR detection comparisons. In terms of achieving fixed probabilities of false alarm and higher probabilities of detection, simulation results show that the new GWO scheme performs better than the BBO method described in the literature in most cases
Citation

M. GOURI Amel, (2019-02-09), "Optimization of Distributed CFAR Detection using Grey Wolf Algorithm", [national] Procedia Computer Science , Elsevier

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2018-10-24

The performance of Decentralized CFAR Detection using Biogeography Based Optimization

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian cluter using Biogeography Based Optimization (BBO) technique is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection
Citation

M. GOURI Amel, (2018-10-24), "The performance of Decentralized CFAR Detection using Biogeography Based Optimization", [international] 5th IEEE CiSt (OMCM) , Marrakech-Morocco.

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2018-02-01

Parameter estimation of CGIG clutter plus noise using constrained NIOME and MLE approaches

Compound Gaussian inverse Gaussian (CGIG) distribution with thermal noise is well suited for sea clutter. The compact expression of higher order moments estimator (HOME) for CGIG clutter plus noise parameters is derived. When non-integer order moments (NIOM) are considered, numerical integration based on ‘Legendre’ and ‘Laguerre’ polynomials of Gauss quadrature method is employed. This estimator is referred to as constrained NIOM estimator (NIOME)-based method. Using the two first intensity moments, a constrained maximum likelihood estimator (CMLE) is formed by minimising the negative log likelihood function in one variable to obtain the shape parameter. For comparison purposes, computer simulations are presented with known and unknown clutter-to-noise ratio (CNR). In the case of known CNR, the method of moments provides a good fit as well as the NIOME and the CMLE methods. For unknown CNR, regardless the computing time, the HOME method is less accurate than the others. The constrained NIOME method requires numerical optimisation with acceptable calculation time. In most cases, the CMLE approach is accurate, but relatively slow due to the calculation of numerical integration of likelihood functions.
Citation

M. GOURI Amel, (2018-02-01), "Parameter estimation of CGIG clutter plus noise using constrained NIOME and MLE approaches", [national] IET Radar , Sonar & Navigation

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2016-12-18

Mixture of compound-Gaussian distributions for radar sea-clutter modeling

Compound Gaussian models are mostly considered for describing radar sea-clutter returns and are the basis of adaptive target detection with false alarm rate regulation. When high resolution radars operate at small grazing angles, the existing compound Gaussian distributions with additive thermal noise could not fit accurately the empirical data in some cases. This communication emphasises on the statistical description of the sea clutter using a mixture of compound inverse Gaussian (CIG) distribution, K distribution and generalized Pareto distribution (GP) with additive thermal noise. Non-linear least squares curve fitting technique based on the Nelder-Mead algorithm is used to find simultaneously optimal parameters values of the mixture model. Experiments comparisons are conducted to show a goodness of fit of the proposed mixture model for modelling the McMaster IPIX backscatter
Citation

M. GOURI Amel, (2016-12-18), "Mixture of compound-Gaussian distributions for radar sea-clutter modeling", [international] th International Conference on Control Engineering & Information Technology (CEIT) , Hammamet-Tunisia

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