A method for estimating parameters of K-distributed clutter

Raghavan, R. S.

doi:10.1109/7.78298

Cited by 109 publications

(49 citation statements)

References 8 publications

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“…They are extensively used for modeling the statistics of interferences of radiowaves, radar clutter, optical scintillation, ultrasound scattering, etc [12,13,14,15,16,17,18,19]. Interestingly, a recent application of inverse-chi-square superstatistics in medical statistics has been proposed [20] with excellent agreement with real data.…”

Section: Main Properties and Characterizationsmentioning

confidence: 99%

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Maximum entropy with fluctuating constraints: The example of K-distributions

Bercher

2008

Physics Letters A

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We indicate that in a maximum entropy setting, the thermodynamic β and the observation contraint are linked, so that fluctuations of the latter imposes fluctuations of the former. This gives an alternate viewpoint to 'superstatistics'. While a Gamma model for fluctuations of the β parameter gives the so-called Tsallis distributions, we work out the case of a Gamma model for fluctuations of the observable, and show that this leads to K-distributions. We draw attention to the fact that these heavy-tailed distributions have high interest in physical applications, and we discuss them in some details.

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Section: Main Properties and Characterizationsmentioning

confidence: 99%

“…It is worth mentioning that the estimation of the parameters of this distribution is well documented, e.g. [14,16,18]. (12) with a = 0.1 to a = 10, and with b = 3a/2 chosen so that the expectation of all distributions is equal to 1.…”

Section: Main Properties and Characterizationsmentioning

confidence: 99%

Maximum entropy with fluctuating constraints: The example of K-distributions

Bercher

2008

Physics Letters A

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“…Several methods for estimating the K distribution parameters have been proposed in the literature [23][24][25][26][27][28][29][30]. In [31], a comparison of four different methods was performed, concluding that the MoM based on second and fourth moments [32] displays the best results.…”

Section: B Methods For Obtaining K Shape Parameter Estimatesmentioning

confidence: 99%

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Fernández¹,

Vidal²

2016

IJIMAI

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-The discrimination of the clutter interfering signal is a current problem in modern radars' design, especially in coastal or offshore environments where the histogram of the background signal often displays heavy tails. The statistical characterization of this signal is very important for the cancellation of sea clutter, whose behavior obeys a K distribution according to the commonly accepted criterion. By using neural networks, the authors propose a new method for estimating the K shape parameter, demonstrating its superiority over the classic alternative based on the Method of Moments. Whereas both solutions have a similar performance when the entire range of possible values of the shape parameter is evaluated, the neuronal alternative achieves a much more accurate estimation for the lower Fig.s of the parameter. This is exactly the desired behavior because the best estimate occurs for the most aggressive states of sea clutter. The final design, reached by processing three different sets of computer generated K samples, used a total of nine neural networks whose contribution is synthesized in the final estimate, thus the solution can be interpreted as a deep learning approximation. The results are to be applied in the improvement of radar detectors, particularly for maintaining the operational false alarm probability close to the one conceived in the design.

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“…The ACF model parameter estimation algorithm assumes that estimates are available for the intensity ACFR I from an image block or window, the mean intensityμ I , the imaging point spread function parametersβ x andβ y estimated via leastsquares fit of the imaging point spread function [7], andν the single-point K-distribution shape parameter found using Raghavan's method [10] or another suitable method. With these estimates as inputs or knowns the remaining parameters (1) are estimated by manipulating the intensity ACF equation intô…”

Section: A Acf Model Parameter Estimationmentioning

confidence: 99%

Autocorrelation features for synthetic aperture sonar image seabed segmentation

Cobb

Prı́ncipe

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. Algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first-and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.

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A method for estimating parameters of K-distributed clutter

Cited by 109 publications

References 8 publications

Maximum entropy with fluctuating constraints: The example of K-distributions

Maximum entropy with fluctuating constraints: The example of K-distributions

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Autocorrelation features for synthetic aperture sonar image seabed segmentation

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