Abstract
The aim of this study is to analyze the influence of neglecting K distributed clutter texture on wideband radar distributed targets detection. At first, the texture and the speckle of K clutter are researched and the Probability Density Function (PDF) of K clutter and its texture are derived, then the optimal detector by Neyman-Pearson (NP) is proposed, by contrast, another detector-Suboptimum Generalized Likelihood Ratio Test (GLRT) neglecting the clutter texture is given. Next, the estimation of covariance matrix is introduced. Finally, the numerical results are presented by means of Monte Carlo simulation strategy and the simulation results highlight that the performance loss of the 2 detectors in different shaping parameter, the result shows that the performance loss of the detector in K distributed clutter less than 1 db due to the texture is neglected and adaptively estimating the covariance matrix and the K clutter texture can be neglected on wideband radar targets detection.
Highlights
The problem of wideband radar spread targets detection has received great attention recently
We have addressed the problem of adaptive detection of range-spread targets in K distributed clutter
We first give the expression of K distributed clutter, analyze the texture of K clutter and propose the Probability Density Function (PDF) of K clutter texture
Summary
The problem of wideband radar spread targets detection has received great attention recently. To compound-Gaussian clutter, it is modeled as the product of 2 independent random quantities CCtt = ττttggtt, the clutter spikiness gt is usually modeled as a compoundGaussian vector (Conte et al, 2000, 2002a, b; Gini et al, 1999, 2002; Bueno et al, 2008; Robey et al, 1992; Miao and Iommelli, 2008; Shuai et al, 2010; Pascal et al, 2008; Bon et al, 2008). It needs to deeply research that whether the facilitation leads a large performance loss to the detector
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