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In this paper, we aim to generalize some of the single-input single-output (SISO) constant false alarm rate (CFAR) detectors, operating in Pareto clutter. That is, for a known scale parameter, we propose to extend to statistical multiple-input multiple-output (MIMO) radars, closed form expressions of the probability of false alarm of the well-known geometric mean (GM-CFAR), greatest of (GO-CFAR) and...
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...
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 (gk(nucirc))of the Raghavanpsilas and ML/MOM (Maximum-Likelihood and Method...
This paper addresses a novel approach based on neuro-fuzzy inference system to solve the estimation problem of the K-distributed parameters. The method is based on a network implementation with real weights and the real genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. The proposed FNN estimator is based on the arithmetic and...
In this correspondence, we introduce a new approach based on fuzzy neural network (FNN) for estimating the parameters of the K-distribution. The FNN proposed estimator combines the Raghavan's and maximum-likelihood and method of moments (ML/MOM) methods and offers a lower variance of parameter estimates when compared with the existing non-maximum-likelihood methods.
This work provides an effective approach based on adaptive neuro-fuzzy inference system to the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the ML-CFAR (maximum-likelihood CFAR) detector in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The genetic learning algorithm...
This paper provides a novel approach based on neuro-fuzzy inference system for the estimation problem of the K-distributed parameters. The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's...
The use of genetic algorithms (GAs) tool for the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics is considered. An approximate expression of the probability of detection (PD) of the ordered statistics (OS)-CFAR detector in Weibull clutter is derived. Optimal threshold values of distributed maximum likelihood (ML)-CFAR detector and distributed OS-CFAR detector...
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