Two novel methods for estimating the compound
            <i>K</i>
            -clutter parameters in presence of thermal noise

Mezache, Amar; Sahed, Mohamed; Laroussi, Toufik; Chikouche, Djamel

doi:10.1049/iet-rsn.2010.0296

Cited by 36 publications

(39 citation statements)

References 13 publications

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“…To this effect, a multidimensional unconstrained nonlinear algorithm, namely, the Nelder-Mead (N-M) method, is used to minimize the residuals between the real data and the fitted curve with unknown parameters. Details of the PCFE method based on the N-M algorithm are found in [15].…”

Section: Overview On Compound K-distribution Plus Noise and Existmentioning

confidence: 99%

“…As shown above for (15), (20) also involves integrals, which can be calculated using [17], eqs. (2.3.3.1) and (2.6.23.4), to yield…”

Section: A Derivation Of Log(z)mentioning

confidence: 99%

“…Using this new idea, the tail of the K-clutter plus noise distribution can be approximated by a noise-free K-distribution. To solve the estimation accuracy inherent to the presence of added noise, Mezache et al [15] introduced a more pragmatic approach based on a parametric curve-fitting estimation method. This approach performs well when the number of the data samples is large enough.…”

Section: Introductionmentioning

confidence: 99%

“…The paper is organized as follows. In Section 2, we briefly recall the compound model of the K-distribution in presence of Gaussian noise and its parameter estimation using higher-order moments estimation (HOME) [6] and parametric curve-fitting estimation (PCFE) [15] methods. In Section 3, we derive expressions for the log-based moments.…”

Section: Introductionmentioning

confidence: 99%

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

Sahed

Mezache

Laroussi

2015

IEEE Trans. Aerosp. Electron. Syst.

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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.

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Section: Overview On Compound K-distribution Plus Noise and Existmentioning

confidence: 99%

“…As shown above for (15), (20) also involves integrals, which can be calculated using [17], eqs. (2.3.3.1) and (2.6.23.4), to yield…”

Section: A Derivation Of Log(z)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Sahed

Mezache

Laroussi

2015

IEEE Trans. Aerosp. Electron. Syst.

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“…Among the different components of clutter modeling, the estimation of the distribution parameters has occupied a prominent place in multiple studies [23][24][25][26]. In the specific case of the Pareto distribution, various estimators have been proposed for the shape parameter that has a remarkable influence in the quality of the detection [21,27,28].…”

mentioning

confidence: 99%

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

Bacallao-Vidal¹,

Machado-Fernández²

2016

IJIMAI

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-The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.

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

2022

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

Cited by 36 publications

References 13 publications

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

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

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

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

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