Parameter estimation for the K-distribution based on [z log(z)]

Blacknell, D.; Tough, R.J.A.

doi:10.1049/ip-rsn:20010720

Cited by 98 publications

(70 citation statements)

References 7 publications

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“…The formula is reported in equation (3). Another approach is based on the estimates of the mean of the data and of the mean of the logarithm of the data, as discussed in chapter 13 of [15] and in [29][30][31], and reported in equation (4) where N is the number of non-coherently integrated pulses.…”

Section: Analysis Of Amplitude Statisticsmentioning

confidence: 99%

Analysis of polarimetric bistatic sea clutter using the NetRAD radar system

Fioranelli

Ritchie

Griffiths

et al. 2016

IET Radar, Sonar & Navigation

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This paper presents a comparative analysis of amplitude and Doppler spectra statistics from monostatic and bistatic sea clutter data at S-band. These data cover a range of bistatic angles from 60° to 120° and include simultaneous monostatic and co-polarized and cross-polarized bistatic recordings, providing direct comparison of their statistical properties. The time series of amplitude data are fitted to the compound K and K+Noise distribution in the range domain and in the Doppler domain, and the variation of the shape parameter is presented for monostatic and co-polarized and cross-polarized bistatic data as an indication of the spikiness of the clutter. From the analysis of this parameter it is shown that cross-polarized bistatic data tend to be less spiky in the range and Doppler domain than the simultaneous co-polarized bistatic data, and than the simultaneous monostatic data, which could be advantageous in terms of radar detection performance, especially for low-visibility maritime targets.

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Section: Analysis Of Amplitude Statisticsmentioning

confidence: 99%

Analysis of polarimetric bistatic sea clutter using the NetRAD radar system

Fioranelli

Ritchie

Griffiths

et al. 2016

IET Radar, Sonar & Navigation

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“…At first, the image raw data are generated using the simulation model (as shown in Section 3.1). For each RF signal, the settings of Nco and Δy pairs are selected as computational parameters, and the Fourier coefficients an are calculated using the RF signal in Equation (8). Subsequently, the probability density function w(y) is reconstructed using the Fourier coefficients in Equation (7), and standard and weighted entropies are estimated using the estimated w(y) in Equations (1) and (2), respectively.…”

Section: Weighted Entropy Estimation Of Ultrasound Datamentioning

confidence: 99%

“…Therefore, some non-Rayleigh distributions, such as Rician [3], K [4], homodyned K [5], and generalized K [6], have been applied to encompass both the pre-Rayleigh and post-Rayleigh statistics. Estimation methods for the parameters of the K models have also been explored [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Detection of Scatterer Concentration by Weighted Entropy

Tsui

2015

Entropy

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Abstract:Ultrasound backscattering signals depend on the microstructures of tissues. Some studies have applied Shannon entropy to analyze the uncertainty of raw radiofrequency (RF) data. However, we found that the sensitivity of entropy in detecting various scatterer concentrations is limited; thus, we propose a weighted entropy as a new information entropy-based approach to enhance the performance of scatterer characterization. A standard simulation model of ultrasound backscattering was used to generate backscattered RF signals with different number densities of scatterers. The RF signals were used to estimate the weighted entropy according to the proposed algorithmic scheme. The weighted entropy increased from 0.08 to 0.23 (representing a dynamic range of 0.15) when the number density of scatterers increased from 2 to 32 scatterers/mm 2 . In the same range of scatterer concentration, the conventional entropy increased from 0.16 to 0.19 (a dynamic range of 0.03). The results indicated that the weighted entropy enables achieving a more sensitive detection of the variation of scatterer concentrations by ultrasound.

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“…  log r X X Estimation Blacknell and Tough in [9] proposed an estimator of  based on   log r X X which gives a comparable accuracy with the fractional moments estimators among others. They noticed that setting leads to simple expressions for the estimator of 1 r   of the one-sided K distribution.…”

Section: 3mentioning

confidence: 99%

“…Iskander et al in [7] propose the use of fractional moments which they show produce estimates with lower variance than the MoM. The authors in [8] and [9] propose the use of logarithmic estimators. ML estimates for a limited range of  were presented by Raghavan in [10] based on an approximation of the K distribution using the Gamma distribution.…”

Section: Introductionmentioning

confidence: 99%

A Low Sample Size Estimator for K Distributed Noise

Alban¹,

Magaña²,

Skinner³

2012

JSIP

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In this paper, we derive a new method for estimating the parameters of the K distribution when a limited number of samples are available. The method is based on an approximation of the Bessel function of the second kind that reduces the complexity of the estimation formulas in comparison to those used by the maximum likelihood algorithm. The proposed method has better performance in comparison with existing methods of the same complexity giving a lower mean squared error when the number of samples used for the estimation is relatively low.

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Parameter estimation for the K-distribution based on [z log(z)]

Cited by 98 publications

References 7 publications

Analysis of polarimetric bistatic sea clutter using the NetRAD radar system

Analysis of polarimetric bistatic sea clutter using the NetRAD radar system

Ultrasound Detection of Scatterer Concentration by Weighted Entropy

A Low Sample Size Estimator for K Distributed Noise

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