Translation to the normal distribution for radar clutter

Lamont-Smith, T.

doi:10.1049/ip-rsn:20000043

Cited by 16 publications

(11 citation statements)

References 22 publications

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“…The sample third-order moment of Y is written as N ny3=-_ (7) ii1 If Y comes from N (0,1) and the sample size N > 100, it is approximate that [5]: m3 N N0 6(N-X2) ) (8) one distribution closely resembles another. In order to describe the distribution characteristics and comparability of different distribution types, the statistics skewness and kurtosis describing probability density curve shape are used here [6]. The skewness and kurtosis of a random variable X are defined, respectively, as Ah= 3Z2'2 /12 /4 2…”

Section: Standard Normal Distribution Testmentioning

confidence: 99%

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Method for Radar Clutter Distribution Test Based on Distribution Transform

Wang

Liu

et al. 2006

2006 CIE International Conference on Radar

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In this paper, we present an effective method for testing radar clutter distribution in the case of short observation sequence. The proposed method firstly transforms a clutter sequence from an unknown distribution by a hypothetic distribution function and the inverse function of standard normal distribution, and then tests whether the transformed sequence comes from a standard normal distribution using simple third-order moment to decide the original sequence distribution type.

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Section: Standard Normal Distribution Testmentioning

confidence: 99%

“…For a sequence from Log-normal distribution, the sequence transformed by the Log-normal DF is written as 0-7803-9582-4/06/$20.00 C2006 IEEE (5) FLN (xi) = [ ln(x')1 xi >0 (i= 1,..., N) (6) where, and u is the mean and standard variance of Inx, respectively. p is scale parameter, and u is shape parameter.…”

Section: Introductionmentioning

confidence: 99%

Method for Radar Clutter Distribution Test Based on Distribution Transform

Wang

Liu

et al. 2006

2006 CIE International Conference on Radar

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“…Various methods have been applied to estimate its parameters [2][3][4][5]. A generalisation of the K distribution is the K plus Rayleigh model [6], where the clutter contains both K and Rayleigh distributed components. If the inverse gamma distribution is used for the clutter power, the compound model is the generalised Pareto distribution, and this has also been used to model radar and sonar clutter [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for Pareto and K distributed clutter with noise

Bocquet

2015

IET Radar, Sonar & Navigation

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The form of the z log z estimator is derived for both Pareto and K distributed clutter plus noise. When noise is included, numerical zero finding is required to obtain the shape parameter from the estimator, but it still provides a robust and accurate method that is relatively quick to compute. It is compared with two other methods. The method of moments is the simplest and fastest to compute, but less accurate than other methods if the clutter shape parameter is small. A constrained maximumlikelihood (ML) estimator is constructed by maximising the log likelihood function in one dimension to find the shape parameter, while holding the mean power and clutter to noise ratio constant. This estimator is robust and accurate, but relatively slow because numerical integration is required to calculate the likelihood function, along with numerical optimisation to find the maximum. If the noise power is unknown, it can be obtained using the first two intensity moments in combination with either the constrained ML or z log z estimator. These combinations provide more robust and accurate estimates than the third intensity moment.

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“…Regarding its heavy computational load, this method can be used as a benchmark to other parameter estimation methods for any kind of distribution. In the last decade, relying upon the work found in [6], Lamont-Smith [20] showed through a heuristic estimation method that the K-distribution requires the addition of a Rayleigh-distributed component, yielding the so-called K plus Rayleigh distribution. Since the Rayleigh component has a non-negligible power level, any parameter estimation method should also take into account this component.…”

Section: Introductionmentioning

confidence: 99%

A novel [z log(z)]-based closed form approach to parameter estimation of k-distributed clutter plus noise for radar detection

Sahed

Mezache

Laroussi

2015

IEEE Trans. Aerosp. Electron. Syst.

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In this paper, we present a novel [z log(z)]-based approach to the evaluation of estimators of the K-distributed clutter plus thermal noise parameters. In doing this, we start by deriving expressions of log-based moments of the received data, i.e., means of [log(z)] and [z log(z)], which are related to the parameters of the K plus noise distribution, the digamma, and the hypergeometric functions. Then, by accommodating a single pulse and noncoherent integration of N pulses, respectively, we first determine the new estimators in terms of log-based moments and first-and second-order moments. As the computation of these nonlinear estimates requires the use of numerical routines, we resort to the inverse of the harmonic mean of the received data to get equivalent but more interesting estimates in which expressions are independent of the confluent hypergeometric functions. Monte Carlo simulations show that the proposed estimators are more efficient than existing methods for various clutter plus noise situations.

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Translation to the normal distribution for radar clutter

Cited by 16 publications

References 22 publications

Method for Radar Clutter Distribution Test Based on Distribution Transform

Method for Radar Clutter Distribution Test Based on Distribution Transform

Parameter estimation for Pareto and K distributed clutter with noise

A novel [z log(z)]-based closed form approach to parameter estimation of k-distributed clutter plus noise for radar detection

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