MAROUA Louglaib

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MAROUA Louglaib

مروة لقليب

maroua.louglaib@univ-msila.dz

0776701634

  • Departement of ELECTRONICS
  • Faculty of Technology
  • Grade PHd
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About Me

Education

Stage de decouverte. in Etablissement Nationale de la Navigation Aerienne

Domain

Science et Technologies

Research Domains

telecommunication systems Radar detection and parameter estimation

Filiere

Electronique

Electronics andTelecommunications

Location

Msila, Msila

Msila, ALGERIA

Code RFIDE

  • 2025-12-10
  • 2025-12-10

    Parameter Estimation and Modeling Performance of Compound-Gaussian Clutter with WeibullDistributed Texture

    This paper addresses the parameter estimation of the compound-Gaussian clutter with Weibull distributed texture (CGWB). CGWB distribution is introduced to represent sea clutter; it is adequate for low-resolution sea clutter at low grazing angles. CGWB distribution is one of the biparametric distributions characterized by two parameters: the shape and scale parameters. In this work, the fractional negative order moment estimator (FNOME) is suggested for CGWB parameter estimation. The accuracy of the FNOME is evaluated using both the Kolmogorov-Smirnov (KS) and the mean square error (MSE) criteria. FNOME performance is compared to existing estimators: method of moments (MoM), method of fractionalorder moments (MoFM), and [zlog(z)]-based method.

    Citation

    Maroua LOUGLAIB , IZZEDDINE Chalabi , Amir Benzaoui, ,(2025-12-10), Parameter Estimation and Modeling Performance of Compound-Gaussian Clutter with WeibullDistributed Texture,2nd International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE2025),M'sila,Algeria

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  • 2025-05-06
  • 2025-05-06

    Machine-Learning-based parameter estimation of Nakagami Texture of Compound Gaussian Clutter

    Accurate and efficient estimation of the shape parameter of Nakagami texture is of considerable interest in wireless communication and radar systems, especially for constant false alarm rate (CFAR) detection. In this paper, two machine learning-based estimators are proposed. Single-layer perceptron estimator with one hidden layer (MLPE-1) and the second is a multi-layer perceptron with two hidden layers estimator (MLPE-2) to capture more complexities. The estimation performance of the proposed estimators (MLPE-1 and MLPE-2) is assessed and compared with the existing maximum likelihood estimator (MLE) and generalized moment estimator (GME). The robustness of each estimator is evaluated using root mean square error (RMSE).

    Citation

    Maroua LOUGLAIB , IZZEDDINE Chalabi , ,(2025-05-06), Machine-Learning-based parameter estimation of Nakagami Texture of Compound Gaussian Clutter,International Conference on Electronics, Energy and Measurement, IC2EM'2025,Algiers,Algeria

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  • 2023-11-23
  • 2023-11-23

    performance analysis of CFAR detectors in pareto clutter

    CFAR (Constant false alarm rate) detection is a procedure that can be implemented in radars. Today, radars cover a wide range of disciplines in various fields such as aeronautics, military, meteorology, automobile traffic, etc. In this context, the aim of this paper is to analyze the performance of the TM-CFAR (Trimmed mean), GM-CFAR (Geometric mean) and OS-CFAR (Order statistic) detectors in Pareto distributed clutter. Comparative study has been carried out using the previous detectors for different situations including homogeneous and heterogeneous clutter.

    Citation

    Maroua LOUGLAIB , IZZEDDINE Chalabi , Amir Benzaoui, ,(2023-11-23), performance analysis of CFAR detectors in pareto clutter,the 1st edition of the international conference on electronics engineering and telecommunications,Bordj Bou Arreridj university , Algeria

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