Comparison of parameter estimators for K-distribution

Blacknell, D.

doi:10.1049/ip-rsn:19949885

Cited by 112 publications

(50 citation statements)

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“…Nevertheless, in the context of electromagnetic scattering by a random medium (Blacknell, 1994;Jakeman, 1987) it has been shown to be an approximation of a K distribution (see Table 3). Actually, for a given Weibull-distributed random variable with parameters a and b it is straightforward to ®nd the closest K distribution with parameters a H and b H by just equating their ®rst and second moments l and r…”

Section: Discussionmentioning

confidence: 99%

Statistical distributions of water content and sizes for clouds above Europe

Berton¹

2000

Ann Geophysicae

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Abstract. The statistics of quantities involved in the synthesis of cloud scenes have been investigated from an original data base. Frequency distributions of ice and water content (IWC), horizontal and vertical sizes (L and H), and top temperatures (T) of clouds above Europe have been derived for nine types of clouds (As, Cb, Ci, Cg, LwCg, OrCg, Cs, Ns, Sc). It appears that the cumulated frequency plots can be well ®tted with log-normal or Weibull pro®les, and that for IWC and T cloud types can be split into two or three classes according to slopes in logarithmic coordinates. Crosscorrelation coecients between IWC, L, H and T have been also derived. Implications for the physics of the cloud build-up processes are brie¯y outlined. Critical analysis and comparison of other published results are proposed.

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Section: Discussionmentioning

confidence: 99%

Statistical distributions of water content and sizes for clouds above Europe

Berton¹

2000

Ann Geophysicae

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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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“…We will write µ and σ 2 to denote the intrinsic (unknown) mean and variance of the amplitudes of K distributed clutter, while for their estimates based on pixel (amplitude) values x, we will use M = <x> and S 2 = <(x − M) 2 >. From µ (estimated by M), σ (estimated by S) and L (provided), ν can be derived from theoretical relations (e.g., [70]). …”

Section: Threshold Settingmentioning

confidence: 99%

The SUMO Ship Detector Algorithm for Satellite Radar Images

et al. 2017

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Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C-and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1-100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU's Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

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Comparison of parameter estimators for K-distribution

Cited by 112 publications

References 0 publications

Statistical distributions of water content and sizes for clouds above Europe

Statistical distributions of water content and sizes for clouds above Europe

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

The SUMO Ship Detector Algorithm for Satellite Radar Images

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