M. MEZACHE Amar

Prof

Directory of teachers

Department

Departement of ELECTRONICS

Research Interests

Radar signal processing applications for parameter estimation and CFAR detection.

Contact Info

University of M'Sila, Algeria

Email : amar.mezache@univ-msila.dz

On the Web:

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Recent Publications

2024-10-31

Introduction to artificial intelligence and is applications in renewable energies: Hands-on examples and tutorials with coding

Artificial Intelligence
Citation

M. MEZACHE Amar, (2024-10-31), "Introduction to artificial intelligence and is applications in renewable energies: Hands-on examples and tutorials with coding", [international] IEEE International Multi-Conference on Smart System and Green Process , Djerba, Tunisia

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Annalysis of non-coherent CFAR detection in CG-LNT Rradar clutter

This communication deals with the problem of non-coherent CFAR (Constant False Alarm Rate) detection in homogeneous and heterogeneous CG-LNT (Compound Gaussian-Lognormal Texture) clutter. The distribution of the latter with and without thermal noise is presented and possesses a strong ability to model various sea-clutter scenes. The performances of CA (Cell Averaging)-, SO (Smallest Of)-, GO (Greatest Of)-and OS (Order Statistic)-CFAR algorithms are investigated for a known standard deviation parameter. In the presence of secondary targets WH (Weber Hykin)- and WHOS-CFAR detectors are also examined where their CFAR properties are shown to be better than those obtained by OS-CFAR procedure.
Citation

M. MEZACHE Amar, (2024-10-31), "Annalysis of non-coherent CFAR detection in CG-LNT Rradar clutter", [international] IEEE International Multi-Conference on Smart System and Green Process , Djerba, Tunisia

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2024-10-01

Extended Order Statistic CFAR Detector in Presence of interfering targets in Log-Normal Clutter

The constant false alarm rate (CFAR) detection is considered in this communication in the presence of secondary targets in log-normal background. From an universal test statistic given as a function of two different scale invariant functions, the modified order statistic (OS) CFAR is given in terms of three ordered statistics. This is obtained by the combination of conventional OS-CFAR and Weber-Haykin (WH) CFAR algorithms. CFAR properties and detection performances of the WHOS-CFAR detector are checked against existing CFAR procedures in favour of homogeneous and heterogeneous clutter. Through Monte-Carlo simulations, it is shown that the proposed WHOS-CFAR detector is robust for a situation of the presence of strong interfering targets.
Citation

M. MEZACHE Amar, (2024-10-01), "Extended Order Statistic CFAR Detector in Presence of interfering targets in Log-Normal Clutter", [international] IEEE International Conference on Advances in Electrical and Communication Technologies (ICAECOT’ 24) , Setif, Algeria

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2024-06-28

CFAR detection using two scale invariant functions in heterogeneous Weibull clutter

In this paper, we considered the constant false alarm rate (CFAR) detector in presence of Swerling 1 interfering targets
surrounded in Weibull background. The proposed CFAR detector is inspired from a general test statistic that is given as
a function of two selected scale invariant functions. It is based on the well known Weber-Haykin Order Statistics CFAR
(WHOS-CFAR) detector and is given in terms of three ranked samples. Even in the face of several interfering targets, the
investigation of the false alarm regulation demonstrates the robustness with respect to scale and shape clutter parameters.
Moreover, through simulated and IPIX (Intelligent PIXel X-band radar) real data, the performance of the above WHOSCFAR
detector is checked for both homogeneous and heterogeneous environments and achieves the CFAR-ness property
with immunity to circumstances involving interfering targets.
Citation

M. MEZACHE Amar, Zebiri Khaled, , (2024-06-28), "CFAR detection using two scale invariant functions in heterogeneous Weibull clutter", [national] Signal Image and Video Processing , Springer

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2024-05-12

Two Scale Invariant Functions based CFAR Detection in Heterogeneous Weibull Clutter

In this communication, we considered the constant false alarm rate (CFAR) detector in presence of Swerling 1 interfering targets surrounded in Weibull background. The proposed CFAR detector is inspired from a general test statistic that is given as a function of two selected scale invariant functions. It is based on the well known Weber-Haykin Order Statistics CFAR (WHOS-CFAR) detector and is given in terms of three ranked cells. Even in the face of several interfering targets, the investigation of the false alarm regulation demonstrate the robustness with respect to clutter parameters. Moreover, the performance of the above CFAR detector was checked for both homogeneous and heterogeneous environments and achieves the CFAR-ness property with immunity to circumstances involving interfering targets.
Citation

M. MEZACHE Amar, Zebiri Khaled, , (2024-05-12), "Two Scale Invariant Functions based CFAR Detection in Heterogeneous Weibull Clutter", [international] IEEE 2nd INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATIC CONTROL IEEE ICEEAC 2024 , Setif, Algeria

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2024-01-09

Quadruple-order statistics CFAR algorithm for Pareto type I environment

In this work, we considered the constant false alarm rate (CFAR) detection in the presence of numerous interfering target
situations in Pareto type I clutter. This new non-coherent CFAR detector is derived, which possessed the CFAR property for
Pareto background. It is labeled Weber–Haykin–Weber–Haykin (WHWH-CFAR) detection algorithm and is based on fourordered
statistics. It achieves the CFAR-ness property with immunity to circumstances involving interfering targets under the
assumption of Pareto type I background. Even in the face of several interfering targets, the examination of the false alarm
regulation demonstrated robustness with respect to clutter parameters. The performance of the proposed detector is illustrated
for both homogeneous and heterogeneous environments. From simulated data and IPIX (Intelligent PIXel X-band radar) real
data, when many powerful secondary targets are present, the proposed detector produces a smaller CFAR-loss than the current
CFAR algorithms.
Citation

M. MEZACHE Amar, Zebiri Khaled, , (2024-01-09), "Quadruple-order statistics CFAR algorithm for Pareto type I environment", [national] Signal Image and Video Processing , Springer

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2023-12-01

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. MEZACHE Amar, (2023-12-01), "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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2023-10-21

Radar Clutter Modeling based on CGIG and Mixture CGIG Distributions

Development of target CFAR (Constant False Alarm Rate) detection schemes requires the specification of radar clutter models. There is why the modeling study of non-stationary radar sea-clutter statistics is a first serious research topic in favor of the above challenge. Several scenes of real data consist of mixture Gaussian and non-Gaussian statistics. To obtain an accurate fitting to IPIX (Intelligent Pixel X-band) sea clutter, compound Gaussian Inverse Gaussian (CGIG) class distribution is considered in this work against standard distributions labeled Weibull, log-normal, Pareto type II and K. Parameters values of the above models are estimated from IPIX data using MLE (Maximum Likelihood Estimation) method and LSA (Least Squares Approximation) method. In most cases of IPIX database, experimental study shows that CGIG and mixture CGIG CCDFs (Complementary Cumulative Distributed Function) can fit empirical CCDF in terms of range cell resolution, antennas polarization and range cell number.
Citation

M. MEZACHE Amar, Zakia Torki, Fouad Chebbara, , (2023-10-21), "Radar Clutter Modeling based on CGIG and Mixture CGIG Distributions", [international] 5th Novel Intelligent and Leading Emerging Sciences Conference (NlLES2023) , Egypt

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Analysis of CFAR Detection in a Pareto Type II Clutter with Multiple Pulses

Constant False Alarm Rate (CFAR) detection with multiple pulses provides improvements in detection performance for homogeneous or heterogeneous background. Considering the integration of multiple pulses, this communication focuses on the extension of the previous decision rules labeled GM-CFAR (Geometric Mean), GO-CFAR (Greatest Of), SO-CFAR (Smallest Of) and OS-CFAR (Order Statistic) procedures already developed for the case of target detection embedded in a Pareto type II clutter with a priori known of the scale parameter. In this work, novel test statistics are obtained in closed forms by applying the logarithmic transformation that enables true Gaussian CFAR processes. In the presence of Swerling II targets, the performances of proposed CFAR detectors are influenced by SCR (Signal-to-Clutter Ratio), number of samples and number of integrated pulses.
Citation

M. MEZACHE Amar, Terki Zakia, Fouad Chebbara, , (2023-10-21), "Analysis of CFAR Detection in a Pareto Type II Clutter with Multiple Pulses", [international] 5th Novel lntelligent and Leading Emerging Sciences Conference (NlLES2O23 , Egypt

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2023-07-13

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

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.
Citation

M. MEZACHE Amar, Taha Hocine Kerbaa, , (2023-07-13), "Multi‑headed deep learning‑based estimator for correlated‑SIRV Pareto type II distributed clutter", [international] EURASIP Journal on Advances in Signal Processing , Springer

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2022

Improved Decentralized SO-CFAR and GO-CFAR Detectors via Moth Flame Algorithm

Optimization of distributed constant false alarm rate (CFAR) system parameters is an essential part in radar detection applications. In this work, the moth flame algorithm (MFO) is proposed as an optimization tool to compute scale factors of distributed Greatest of-CFAR (GO- CFAR) and Smallest of-CFAR (SO-CFAR) detectors in presence of Gaussian disturbance. Local binary decisions are obtained firstly from different sensors, at the fusion center, a fusing rule is applied to obtain a global decision. Detection performances comparisons are conducted against previous works using Gray Wolf Optimization (GWO) and Biography Based Optimization (BBO) methods. Simulation results show that the proposed optimizer demonstrates a slight superiority in some cases for ensuring fixed probability of false alarm and higher detection probabilities.
Citation

M. MEZACHE Amar, (2022), "Improved Decentralized SO-CFAR and GO-CFAR Detectors via Moth Flame Algorithm", [international] 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE) , M'sila, Algeria

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On the Performance of GLRT-LTD CFAR Processor in Correlated Pareto Clutter Under Different Estimators

Pareto type II distribution is a class of high-resolution sea-reverberation data models. Application of the GLRT-LTD (Generalized Likelihood Ratio Test Linear Threshold Detector) algorithm requires an accurate estimation of the clutter parameters. Under the assumption of correlated Pareto clutter, several estimators could be applied. In this work, we investigate the effect of the MLE (Maximum likelihood Estimation), Integer order moments, fractional-order moments, and zlog(z) estimators on the detection performance of the GLRT-LTD procedure. From simulated datasets, it is shown that approximate results are obtained by MLE and zlog(z) methods. Moreover, the zlog(z) approach is advantageous when complicated parameter estimation scenarios occur (i.e., correlation coefficient tends to one).
Citation

M. MEZACHE Amar, (2022), "On the Performance of GLRT-LTD CFAR Processor in Correlated Pareto Clutter Under Different Estimators", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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Effect of Non-Integer Order Moments on Parameter Estimation of Pareto Distributed Clutter plus Noise

In this paper, accurate estimate of the Pareto plus noise shape parameter using a modified non-integer positive and negative order moment estimator is investigated. Closed form of the NIPNOME is derived in a previous work [16]. In single pulse case with a fixed value of the order moment, undesirable shape parameter estimates are obtained for high shape parameter values. In the prospect of improving the estimation performance, the NelderMead algorithm is used to optimize two parameters fitness function involving the shape and the non-integer order moment of the considered model. The impact of the order moment on the NIPNOME is examined firstly using simulated Pareto plus noise data. Then, via IPIX database, fitting comparisons are carried out using Pareto plus noise PDFs and CCDFs where [zlog(z)] and NIPNOME are employed to elucidate the robustness of the proposed estimator
Citation

M. MEZACHE Amar, (2022), "Effect of Non-Integer Order Moments on Parameter Estimation of Pareto Distributed Clutter plus Noise", [international] 19th IEEE International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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

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.
Citation

M. MEZACHE Amar, (2022), "Modeling and Parameter Estimation of Radar Sea-Clutter with Trimodal Gamma Population‏", [national] Journal of Telecommunications and Information Technology , JTIT

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On the performance of non-coherent CFAR detectors in sea-clutter: A comparison study

In radar systems, detection performance depends on assumed target and clutter statistical distributions. The probability of detection is shown to be sensitive to the degree of estimation accuracy of clutter levels. In this work, the performances of non-coherent logt-CFAR, zlog(z)-CFAR and Bayesian-CFAR detectors are investigated using both simulated and real data. Three clutter disturbances are considered named log-normal, Weibull and Pareto type II. Based on simulated data, existing CFAR algorithms provide fully CFAR decision rules. From IPIX real data, the dependence of the false alarm probability associated to each detector is studied. With different range resolutions, it is shown that the Bayesian-CFAR algorithm exhibits a small deviation of the false alarm probability.
Citation

M. MEZACHE Amar, (2022), "On the performance of non-coherent CFAR detectors in sea-clutter: A comparison study", [national] International Journal of Information Science & Technology , OJS

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Modeling of High-Resolution Radar Sea Clutter Using Two Approximations of the Weibull Plus Thermal Noise Distribution‏

In radar detection applications, it is important to select the best statistical distribution of clutter for constructing appropriate target detection algorithms. In this work, we propose to model the amplitude statistics of the high-resolution radar by two approximated models of Weibull plus thermal noise (WN) which are considered as compound models with the speckle and texture following a Weibull distribution. First, the overall probability density function and the complementary cumulative distribution function (CCDF) are derived in the integral form as well as the expression of the moments. Then, the estimation of the parameters for each model is conducted using the parametric curve fitting estimation method of the CCDF function based on the Nelder–Mead algorithm and the moments matching method. From the real intelligent pixel processing X-band (IPIX) sea data, the fitted curves of the WN models are evaluated and compared to those of the compound Gaussian plus thermal noise (CGN) models. The modeling experiments are worked out and showed that the proposed approximated models match accurately sea clutter returns compared to CGN models, especially in the tail region.
Citation

M. MEZACHE Amar, (2022), "Modeling of High-Resolution Radar Sea Clutter Using Two Approximations of the Weibull Plus Thermal Noise Distribution‏", [national] Arabian Journal for Science and Engineering , Springer

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Triple-order statistics-based CFAR detection for heterogeneous Pareto type I background‏

For non-stationary radar returns, constant false alarm rate (CFAR) schemes are derived to maintain the probability of false alarm in its desired value. Non-coherent CFAR processes based upon scale and power-invariant distributions for sea clutter received a lot of attention and it is still an open research area in radar target detection. In this context, a novel CFAR detector-labeled WHOS-CFAR (Weber–Haykin-Order Statistics) is proposed in this paper for a Pareto type I sea clutter. The decision rule is given in terms of three ranked samples allowing full CFAR properties with respect to clutter shape and scale parameters. Through Monte-Carlo simulations, the detection performances are illustrated with and without interfering targets. It is shown that the WHOS-CFAR keeps almost the CFAR property compared to existing logt- WH-, GMOS- (Geometric Mean-Order Statistic), TMOS-(Trimmed Mean-Order Statistic) and IE (Inclusion/Exclusion)-CFAR detectors.
Citation

M. MEZACHE Amar, (2022), "Triple-order statistics-based CFAR detection for heterogeneous Pareto type I background‏", [national] Signal, Image and Video Processing , Springer

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2021

Radar CFAR detection for multiple-targets situations for Weibull and log-normal distributed clutter

In the presence of Weibull clutter, the development of sliding window detection processes, based on scale and power invariant distributions, has been extensively examined. This involves the selection of two functions labeled as scale-invariant and secondary CRP (clutter range profile) functions. However, due to the presence of outliers, existing CFAR (constant false alarm rate) algorithms show remarkable CFAR losses. We resort in this work to the practice of a suitable choice of these two functions in order to have a new decision rule with immunity against interfering targets. To do this, a quadruple-order statistics-based CFAR detection algorithm with different four-order statistics are proposed in the presence of log-normal and Weibull clutter disturbances. Via Monte Carlo simulations, the analysis of the false alarm regulation of the proposed detector is studied showing its robustness with respect to clutter parameters. Moreover, for comparison purposes with existing CFAR algorithms, simulated results indicate that lowest CFAR losses can be obtained by the proposed quadruple-order statistics named WHWH-CFAR (Weber–Haykin-Weber–Haykin) in the presence of strong interfering targets. IPIX real data are also performed to test the validity of the proposed detector.
Citation

M. MEZACHE Amar, (2021), "Radar CFAR detection for multiple-targets situations for Weibull and log-normal distributed clutter", [national] Signal, Image and Video Processing , Springer

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2020

Analysis of non-coherent CFAR detectors in sea-clutter: A comparison

In radar systems, detection performance is always related to target and clutter models. The probability of detection is shown to be sensitive to the degree of estimation accuracy of clutter levels. In this work, the performances of logt-CFAR, zlog(z)-CFAR and Bayesian-CFAR detectors are investigated using both simulated and real data. The clutter is assumed to be log-normal, Weibull or Pareto type II distributed. The dependence of the false alarm probability is presented. From simulated data, CFAR detectors provide fully CFAR decision rules. From IPIX real data with different range resolutions, it is shown that the Bayesian-CFAR algorithm exhibits a small deviation of the false alarm probability.
Citation

M. MEZACHE Amar, (2020), "Analysis of non-coherent CFAR detectors in sea-clutter: A comparison", [international] 6th IEEE Congress on Information Science and Technology (CiSt) , Agadir - Essaouira, Morocco

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CNN-LSTM Based Approach for Parameter Estimation of K-Clutter Plus Noise

This paper concerns the problem of estimating the parameters of the K plus noise distribution. In a previous work, it has been shown that, in the multilook scenario, the modified fractional order moment estimator (MFOME) has about the same estimation accuracy as the [zlog(z)] method, but lower computational complexity. However, significant estimation errors have been observed in the single look scenario, low sample size, and large values of the K-distribution shape parameter. Moreover, the computational complexity of the [zlog(z)] estimator discourages its implementation in practical applications. The aim of this work is to estimate the shape parameter of the K-distribution with reduced computational complexity. The problem can be formulated as a supervised many-to-one sequence prediction. We propose here a hybrid model including convolutional and long-short-term-memory (LSTM) neural networks (NN). Estimation performance is investigated by processing both simulated and real clutter data.
Citation

M. MEZACHE Amar, (2020), "CNN-LSTM Based Approach for Parameter Estimation of K-Clutter Plus Noise", [international] IEEE Radar Conference (RadarConf20) , Florence, Italy

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Two novel radar detectors for spiky sea clutter with the presence of thermal noise and interfering targets‏

In the context of noncoherent detection and high-resolution maritime radar system with low grazing angle, new Constant False Alarm Rate (CFAR) decision rules are suggested for two Compound Gaussian (CG) clutters namely: The K distribution and the Compound Inverse Gaussian (CIG) distribution, which are considered among the most appropriate models for sea clutter. The proposed decision rules are then modified to deal with the presence of thermal noise and interfering targets. The proposed detectors are investigated on the basis of synthetic data as well as real data of the IPIX radar database. The obtained results exhibit a high probability of detection as well as an excellent false alarm rate regulation especially for spiky clutter.
Citation

M. MEZACHE Amar, (2020), "Two novel radar detectors for spiky sea clutter with the presence of thermal noise and interfering targets‏", [national] Turkish Journal of Electrical Engineering and Computer Sciences , TUBITAK

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

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. MEZACHE Amar, (2020), "Radar CFAR detection in Weibull clutter based on zlog (z) estimator‏", [national] Remote Sensing Letters , Taylor & Francis

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Iterative maximum likelihood estimation of the compound inverse Gaussian clutter parameters

This Letter aims at proposing a cost-effective method for estimating the parameters of the compound inverse Gaussian distributed clutter. Under the assumption of the absence of thermal noise, an iterative maximum likelihood estimator (IMLE) is proposed and compared with existing MLE, the [zlog(z)] estimator, the non-integer order moments estimator and the higher order moment estimator. The results obtained show that the IMLE method outperforms all the other methods and has similar estimation performance than the MLE method but requires lower computational time.
Citation

M. MEZACHE Amar, (2020), "Iterative maximum likelihood estimation of the compound inverse Gaussian clutter parameters", [national] Electronics Letters , IET

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Parameter Estimation of Rayleigh-Generalized Gamma Mixture Model

The estimation problem of three parameters Rayleigh-Generalized Gamma Mixture (R-GG) radar clutter model is addressed in this paper. Expressions of integer order moments, non-integer order moments and logarithmic moments are presented in such away the scale parameter of the R-GG probability density function (PDF) is eliminated and a two-dimensional estimators labeled HOME, NIOME and [zlog(z)] methods are obtained. Due to the presence of gamma function with fractional variables, these estimators cannot be given in closed forms. The fitness function for each estimator is given as a sum of squared errors of nonlinear equations. Using a numerical routine based upon the simplex search algorithm, the proposed methods were tested firstly on artificial data. Tail fitting of the R-GG model and the standard K-distribution (i.e., special case of the R-GG) is assessed against recorded radar data. The accuracy of the R-GG model and the proposed estimation methods is satisfactory, with the most accuracy of the [zlog(z)] method.
Citation

M. MEZACHE Amar, (2020), "Parameter Estimation of Rayleigh-Generalized Gamma Mixture Model", [national] Instrumentation Mesure Métrologie , IIETA

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Parameter Estimation in Radar K-Clutter Plus Noise Based on Otsu’s Algorithm

In a previous work, it has been shown that the application of a modified fractional order moment (MFOM) estimator leads to the same accuracy as the [zlog(z)] method with lower computation complexity. However, undesirable estimation performances have been observed for single look data, low sample sizes and large values of the K-distribution shape parameter. Moreover, the application of positive and negative order moments estimators (PNOME) has a serious impact on the estimation accuracy of the shape parameter. To reduce this sensitivity, it is important to apply thresholding approaches in the case of a single pulse transmission. To this effect, single and double thresholding estimators are proposed in this paper and the Otsu’s algorithm is used to compute underlying thresholds. On the basis of Monte-Carlo simulation, the performances of the proposed estimators are assessed against moments and [zlog(z)] methods. Experiment examples indicate that the thresholding approaches based on the Otsu’s algorithm is more accurate with computational advantages than existing estimators.
Citation

M. MEZACHE Amar, (2020), "Parameter Estimation in Radar K-Clutter Plus Noise Based on Otsu’s Algorithm", [national] Ingénierie des systèmes d’ information , IIETA

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2019

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. MEZACHE Amar, amel Gouri, , (2019), "Optimization of Distributed CFAR Detection using Grey Wolf Algorithm", [national] Procedia Computer Science , Elsevier

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Pietra index based processor for a heterogeneous Pareto background

This study addresses the problem of automatic target detection in a heterogeneous Pareto background. To achieve
this, the Pietra index based and constant false alarm rate processor (CFAR) is conceived. Specifically, assuming a non
stationary Pareto background with the presence or not of any clutter edge or interfering targets, the Pietra index and the log
geometric mean ratio statistic tests are concomitantly used to allow the proposed processor to switch dynamically to the
appropriate detector; i.e. the geometric mean-CFAR, the greatest of-CFAR or the trimmed mean-CFAR, where all of these
detectors assume a priori unknown scale parameter. That is, according to the outcomes of the Window Selection Probability, the
background level is systematically estimated through the preselected detector. The detection performances of the proposed processor are assessed, via Monte Carlo simulations, in multiple target and clutter edge situations.
Citation

M. MEZACHE Amar, Ali Mehanaoui, Toufik Laroussi, , (2019), "Pietra index based processor for a heterogeneous Pareto background", [national] IET Radar, Sonar & Navigation , The Institution of Engineering and Technology 2019

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Effect of fractional order moments on parameter estimation of K-Clutter plus noise

Parameter estimation of radar clutter is considered as a critical task for the development of target detectors. This work covers the shape parameter estimation of K-clutter plus noise using a modified fractional order moments based approach (MFOME). Closed form of the FOME with fixed fractional order moment is derived in a previous work [11] where undesirable estimation errors are produced in some cases with single look data and low sample sizes. In order to achieve better estimation performance, the fractional order moment and the shape parameter should be optimized together. To this effect, a numerical formula of the corresponding fitness function is given and unconstrained nonlinear optimization method based on the Nelder-Mead simplex algorithm is used to compute the unknown parameters. Via simulated K-clutter plus noise data, the effect of the fractional order on the estimation accuracy is studied firstly. Then, comparisons with existing HOME, FOME and [zlog(z)] methods are conducted to illustrate the efficiency of the proposed estimator
Citation

M. MEZACHE Amar, Taha Hocine Kerbaa, , (2019), "Effect of fractional order moments on parameter estimation of K-Clutter plus noise", [national] 6th International Conference on Image and Signal Processing and their Applications (ISPA), , Mostaganem Algeria

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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. MEZACHE Amar, amel.gouri@univ-msila.dz, , (2019), "Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool", [national] International Journal of Information Science & Technology , https://innove.org

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Model Selection of Sea Clutter Using Cross Validation Method

This work concerns a model selection of sea radar clutter used for adaptive target detection. Three distributions without thermal noise are considered; K, Pareto type II and compound Gaussian inverse Gaussian (CG-IG) with scale and shape parameters. The model selection is carried out by comparing the experimental complementary cumulative distribution function (CCDF), drawn from the recorded data intensity, to a set of the CCDF curves derived from the underling models. To do this, the cross validation technique is used after dividing a set of data into four segments. The best distribution is selected in which the mean of the means square of errors (MSEs) between the real CCDF curve and the fitted CCDF curve is minimal. To select a suited statistical model in most cases, fitting comparisons are illustrated through Intelligent PIxel X-band radar database (IPIX). From this study, it is shown that the appropriate model is?
Citation

M. MEZACHE Amar, taha Houcine.kerbaa@univ-msila.dz, , (2019), "Model Selection of Sea Clutter Using Cross Validation Method", [national] Procedia Computer Science , ELSEVIER

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Effect of Fractional Order Moments on Parameter Estimation of K-Clutter plus Noise

Parameter estimation of radar clutter is considered as a critical task for the development of target detectors. This work covers the shape parameter estimation of K-clutter plus noise using a modified fractional order moments based approach (MFOME). Closed form of the FOME with fixed fractional order moment is derived in a previous work [11] where undesirable estimation errors are produced in some cases with single look data and low sample sizes. In order to achieve better estimation performance, the fractional order moment and the shape parameter should be optimized together. To this effect, a numerical formula of the corresponding fitness function is given and unconstrained nonlinear optimization method based on the Nelder-Mead simplex algorithm is used to compute the unknown parameters. Via simulated K-clutter plus noise data, the effect of the fractional order on the estimation accuracy is studied firstly. Then, comparisons with existing HOME, FOME and [zlog(z)] methods are conducted to illustrate the efficiency of the proposed estimator.
Citation

M. MEZACHE Amar, (2019), "Effect of Fractional Order Moments on Parameter Estimation of K-Clutter plus Noise", [international] 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA) , Mostaganem, Algeria

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A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module

The use of an adequate model of photovoltaic module for the energy prediction is an important tool. To this end, PV modeling primarily involves the formulation of the non-linear current versus voltage (I-V) curve. This paper presents an application of the Nelder-Mead simplex search method for identifying the parameters of solar cell and photovoltaic module models. The proposed technique is used to identify the unknown model parameters, namely, the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. The extracted parameters have been tested against several static IV characteristics of the PV module collected at different operating condition. Comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness of the proposed approach. A dynamic MPP model has also been derived and simulated using the extracted parameters against MPP real dynamic measurements of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers.
Citation

M. MEZACHE Amar, (2019), "A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module", [national] International Journal of Information Science & Technology, IJIST, , ijist

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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. MEZACHE Amar, (2019), "Distributed CA-CFAR and OS-CFAR detectors mentored by biogeography based optimization tool", [national] International Journal of Information Science & Technology , IJIST

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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. MEZACHE Amar, (2019), "Optimization of distributed CFAR detection using Grey Wolf algorithm", [national] Procedia Computer Science , Sciencedirect

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Model selection of sea clutter using cross validation method

This work concerns a model selection of sea radar clutter used for adaptive target detection. Three distributions without thermal noise are considered; K, Pareto type II and compound Gaussian inverse Gaussian (CG-IG) with scale and shape parameters. The model selection is carried out by comparing the experimental complementary cumulative distribution function (CCDF), drawn from the recorded data intensity, to a set of the CCDF curves derived from the underling models. To do this, the cross validation technique is used after dividing a set of data into four segments. The best distribution is selected in which the mean of the means square of errors (MSEs) between the real CCDF curve and the fitted CCDF curve is minimal. Fitting comparisons of models are illustrated through overall data of Intelligent PIxel X-band radar (IPIX). From this study, it is shown that the Pareto type II distribution is suited in several cases of a low cell resolution. On the other hand, the K and CG-IG models characterize generally sea clutter with medium and high cell resolutions.
Citation

M. MEZACHE Amar, (2019), "Model selection of sea clutter using cross validation method", [national] Procedia Computer Science , Sciencedirect

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Statistical Analysis and New Modeling of Real Clutter Signal in FM Radio-based Passive Radars

The major problematic in the design of a competitive detector is the construction of a statistical model that fits real data. In this paper, we present a new theoretical model that describes real data from an FM (Frequency Modulation) radio-based passive bistatic radar. In doing this, we aim to estimate the model parameters using a conventional technique and two optimization numerical methods; namely, the trust-region-reflective approach for non-linear minimization subject to bounds and the direct search Method based on the Nelder-Mead (N-M) algorithm.
Citation

M. MEZACHE Amar, (2019), "Statistical Analysis and New Modeling of Real Clutter Signal in FM Radio-based Passive Radars", [international] IEEE 6th International Conference on Image and Signal Processing and their Applications (ISPA) , Mostaganem, Algeria

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Pietra index based processor for a heterogeneous Pareto background‏

This study addresses the problem of automatic target detection in a heterogeneous Pareto background. To achieve this, the Pietra index based and constant false alarm rate processor (CFAR) is conceived. Specifically, assuming a non-stationary Pareto background with the presence or not of any clutter edge or interfering targets, the Pietra index and the log geometric mean ratio statistic tests are concomitantly used to allow the proposed processor to switch dynamically to the appropriate detector; i.e. the geometric mean-CFAR, the greatest of-CFAR or the trimmed mean-CFAR, where all of these detectors assume a priori unknown scale parameter. That is, according to the outcomes of the Window Selection Probability, the background level is systematically estimated through the preselected detector. The detection performances of the proposed processor are assessed, via Monte Carlo simulations, in multiple target and clutter edge situations.
Citation

M. MEZACHE Amar, (2019), "Pietra index based processor for a heterogeneous Pareto background‏", [national] Radar, Sonar & Navigation , IET

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Estimators of compound Gaussian clutter with log-normal texture‏

Estimators of clutter models parameters based upon higher order moments estimator (HOME) produce usually poor results in particular for low sample sizes. In an attempt to remedy this situation, closed forms of [zlog(z)] and fractional order moments estimator (FOME) are derived in this work and yield a good estimation accuracy of parameters of the compound Gaussian clutter with log-normal texture (CG-LNT). Using simulated and real data, estimation comparisons show that best values of mean square error (MSE) and bias are achieved using the proposed procedures.
Citation

M. MEZACHE Amar, (2019), "Estimators of compound Gaussian clutter with log-normal texture‏", [national] Remote Sensing Letters , Taylor & Francis

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2018

Closed-form estimators of CGIG distributed parameters

The interpolation method has been employed in non-integer-order moments estimators obtained in a previous work for the parameters of compound-Gaussian clutter model with inverse Gaussian texture (CGIG). Closed-form parameter estimators for the CGIG distributed clutter are derived with no special functions. These are achieved after a simple combination of three basic expressions: non-integer positive moments, non-integer negative moments and Bessel recurrence relation. Simulation results show that the derived estimators are well suited for multilook data and have a comparable accuracy as well as the numerically solved maximum-likelihood approach, but they are much simpler and faster to compute
Citation

M. MEZACHE Amar, (2018), "Closed-form estimators of CGIG distributed parameters", [national] Electronics Letters , IET

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Performance of Non-Parametric CFAR Detectors in Log-Normal and K Radar clutter

In this work, the performance of logt-, GMOS(Geometric Mean Order Statistic), TMOS(Trimmed MOS) and IE-CFAR (Inclusion/Exclusion) detectors are investigated in presence of log-normal and K distributed clutter. First, for a finite number of clutter samples, dependence of the false alarm probability P FA upon clutter parameters is examined. The CFAR property for the case of log-normal clutter is maintained while the P FA depends somewhat on the shape parameter of the K distribution. Then, by carrying out Monte-Carlo simulations, we show that for the case of log-normal clutter a small detection difference exists between the underlying CFAR detectors. In the case of K-distributed clutter, there is a significant detection difference for small values of the shape parameter (spiky clutter case).
Citation

M. MEZACHE Amar, (2018), "Performance of Non-Parametric CFAR Detectors in Log-Normal and K Radar clutter", [international] 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM) , Algiers, Algeria

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2017

Parameter estimation for compound-Gaussian clutter with inverse-Gaussian texture

This study deals with the estimation problem of the inverse-Gaussian (IG)–compound-Gaussian (CG) distributed clutter parameters. Under the assumption of the absence of thermal noise, non-integer moments, log-moments and maximum-likelihood-based approaches are employed for model parameter estimation in the cases of single pulse and non-coherent integration of N pulses. The forms of the non-integer-order moments estimator and the [z log(z)] estimator are derived in terms of the Bessel and the exponential–integral functions, respectively. These formulas maintain monotonic nature over all values of the ratio between the shape parameter and the mean clutter power. For a single look data, the ML estimate combined with the mean clutter power is constructed in one dimension search for the shape parameter. By accommodating the mean square error metric, the estimation assessments are investigated via simulated and real data. Authors’ results illustrate that the derived non-integer-order moments estimator is asymptotically efficient for the IG–CG distributed clutter parameters particularly in heavy tailed clutter situations.
Citation

M. MEZACHE Amar, Ahmed Bentoumi, , (2017), "Parameter estimation for compound-Gaussian clutter with inverse-Gaussian texture", [national] IET Radar, Sonar & Navigation , IET

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Closed-form estimators for the Pareto clutter plus noise parameters based on non-integer positive and negative order moments

In this study, closed-form expressions of the parameters estimators for Pareto distributed clutter plus noise are derived. To achieve this, the authors combine two statistical ratios based principally on non-integer positive and negative order moments. This allows absolute elimination of all related special functions contained in some existing estimators. For realistic applications, the proposed non-integer positive and negative order moments based estimators are suited to single-look and multi-look data. Simulation results show that the new estimators not only substantially reduce the computational requirements, but also yield accurate and lower variances parameters compared with the methods of moments and z log z obtained in previous works especially in the situations of heavy-tailed clutter.
Citation

M. MEZACHE Amar, Faouzi Soltani, , (2017), "Closed-form estimators for the Pareto clutter plus noise parameters based on non-integer positive and negative order moments", [national] IET Radar, Sonar & Navigation , IET

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Closed-form fractional-moment-based estimators for K-distributed clutter-plus-noise parameters

In this paper, the problem to estimating the parameters of the K-distributed clutter plus noise is addressed. The existing nonasymptotic z-log-z estimators are only applicable to multiple-pulse data due to the use of the harmonic mean, which is undefined for single-pulse reverberation data. When either single- or multiple-pulse data are used, new closed-form estimators are derived in this work. They combine two interesting statistical ratios based on fractional positive- and negative-order moments so that the hypergemetric functions are absolutely eliminated. For single-pulse transmission, the proposed estimators demonstrate computational advantages compared to the existing approaches. Besides, in the multiple-pulse case, they yield about the same accuracy as the closed-form z-log-z-based estimators obtained in previous work.
Citation

M. MEZACHE Amar, (2017), "Closed-form fractional-moment-based estimators for K-distributed clutter-plus-noise parameters", [national] IEEE Transactions on Aerospace and Electronic Systems , IEEE

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2015

A novel [z log (z)]-based closed form approach to parameter estimation of K-distributed clutter plus noise for radar detection

In this paper, we present a novel [z log(z)]-based approach to the evaluation of estimators of the K-distributed clutter plus thermal noise parameters. In doing this, we start by deriving expressions of log-based moments of the received data, i.e., means of [log(z)] and [z log(z)], which are related to the parameters of the K plus noise distribution, the digamma, and the hypergeometric functions. Then, by accommodating a single pulse and noncoherent integration of N pulses, respectively, we first determine the new estimators in terms of log-based moments and first- and second-order moments. As the computation of these nonlinear estimates requires the use of numerical routines, we resort to the inverse of the harmonic mean of the received data to get equivalent but more interesting estimates in which expressions are independent of the confluent hypergeometric functions. Monte Carlo simulations show that the proposed estimators are more efficient than existing methods for various clutter plus noise situations.
Citation

M. MEZACHE Amar, Toufik Laroussi, , (2015), "A novel [z log (z)]-based closed form approach to parameter estimation of K-distributed clutter plus noise for radar detection", [national] IEEE Transactions on Aerospace and Electronic Systems , IEEE

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Analysis of CFAR detection with multiple pulses transmission case in Pareto distributed clutter

In this work, we extend the performance analysis of some single-pulse transmission signal processors that produce CFAR under the assumption of Pareto distributed clutter for more realistic approach where non coherent integrated pulses are transmitted. A logarithmic transformation approach, that enables true Gaussian CFAR processes to be translated for target detection in Pareto clutter scenario, is used. Consequently, the resulting CFAR processes depend all explicitly on the Pareto scale parameter. Thus, it will be assumed that this parameter is fully known. In the case of multi looks case, analytic expressions for the false alarm probability of the Geometric Mean (GM)-, Greatest Of (GO)- and Smallest Of (SO)-CFAR schemes are derived. Through simulated data, we evaluate the superiority of these developed CFAR detectors against the existing single-pulse CFAR detectors.
Citation

M. MEZACHE Amar, (2015), "Analysis of CFAR detection with multiple pulses transmission case in Pareto distributed clutter", [international] 2015 4th International Conference on Electrical Engineering (ICEE) , Boumerdes, Algeria

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2011

Two novel methods for estimating the compound K-clutter parameters in presence of thermal nois

In this study, the authors present two novel methods to estimate the parameters of the compound K-clutter in presence of additive thermal noise. Based on the parametric fitting to the tail of the clutter distribution, the first method estimates simultaneously the unknown parameters. This is achieved by comparing the experimental cumulative distribution function, drawn from the recorded data intensity, to a set of curves derived from the mathematical model. To this effect, a multidimensional unconstraint non-linear algorithm; namely the Nelder–Mead method is used to minimise the residuals between the real data and the fitted curve with unknown parameters. Considering always the presence of thermal noise and based on the neuronal approaches and fuzzy inference systems, the second method also yields an accurate estimation and guarantees an inexpensive computation of the unknown parameters when the clutter-to-noise ratio (CNR), is known a priori. To assess the obtained results, the authors illustrate the effectiveness of these new methods through Monte-Carlo simulations.
Citation

M. MEZACHE Amar, Toufik Laroussi, Djamel Chikouche, , (2011), "Two novel methods for estimating the compound K-clutter parameters in presence of thermal nois", [national] IET radar, sonar & navigation , IET

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2010

Parameter Estimation in K-Distributed Clutter with Noise using Nonlinear Networks

In this paper, we have introduced a method for estimating the parameters of the sea clutter compound K-distribution in presence of thermal noise by using the artificial neural networks (ANN) and the fuzzy neural networks (FNN) multi inputs/ single outputs (MISO) approaches. In order to accurate more the estimation, the FNN multi inputs/ multi outputs (MIMO) is also considered in this work to estimate simultaneously the shape and the scale parameters of the compound K-distribution. Using the back propagation (BP) algorithm and the genetic learning algorithm (GA), the proposed estimation procedures are trained based on the moments and the ratio of arithmetic and geometric means of the samples. However, the estimation accuracy obtained by these techniques are validated and compared for various values of the shape parameter with different sample sizes.
Citation

M. MEZACHE Amar, (2010), "Parameter Estimation in K-Distributed Clutter with Noise using Nonlinear Networks", [national] Journal of Automation & Systems Engineering , JASE

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2009

K-distribution parameters estimation based on the Nelder-Mead algorithm in presence of thermal noise

In this paper, we propose an efficient approach to the estimation of the compound K-distribution parameters in presence of additive thermal noise. This is acquired by means of a multidimensional unconstrained nonlinear minimization algorithm based upon the Nelder-Mead direct search method. In doing this, we minimize the sum of squared residuals. The best fit is simply achieved by a direct comparison of the experimentally measured cumulative distribution function (CDF) of the recorded data with the set of curves derived from the model of interest. A good minimization can be reached only if the real CDF is accurately estimated. We show, particularly, that the new approach yields the best spiky clutter parameter estimation. The proposed estimator is more efficient than existing estimation methods.
Citation

M. MEZACHE Amar, (2009), "K-distribution parameters estimation based on the Nelder-Mead algorithm in presence of thermal noise", [international] 2009 International Conference on Advances in Computational Tools for Engineering Applications , Zouk Mosbeh, Lebanon

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2008

A Method for Estimating the Parameters of the K-Distribution Using a Nonlinear Network Based on Fuzzy System and Neural Networks

This paper investigates a new technique for estimating the shape parameter of a K-distribution based on fuzzy neural network (FNN). In order to improve the estimation accuracy with inexpensive computational requirement, the FNN estimator is used to accurate the solutions of the nonlinear equations and the inverse functions (g k (nucirc))of the Raghavanpsilas and ML/MOM (Maximum-Likelihood and Method Of Moments)methods respectively. A long this line, the estimated arithmetic and geometric means of data and the estimated function g k (nucirc) of the two estimators are combined and modeled by the FNN shape parameter estimator where an off-line optimization of their weights via genetic algorithms (GA) is considered. The simulation results are carried out to demonstrate the validity of the approach as well as the successfulness of the FNN estimator for low mean square error (MSE) of parameter estimates when compared with existing Raghavanpsilas, HOFM (Higher Order and Fractional Moments), ML/MOM and [(z)log(z)] estimators. Additionally, the FNN method yields parameter estimates with lower computational complexity which allows rapid calculation in real time implementation.
Citation

M. MEZACHE Amar, (2008), "A Method for Estimating the Parameters of the K-Distribution Using a Nonlinear Network Based on Fuzzy System and Neural Networks", [international] 2008 2nd International Conference on Signals, Circuits and Systems , Monastir, Tunisia

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