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In this correspondence, fractional negative-order moments are used to estimate the K-clutter plus noise parameters. Combining the fractional positive and negative moments, we aim to improve the accuracy of the estimation outcomes. This is achieved through the reduction of the effect of the two first intensity moments involved in the equation of the unknown parameters. For instance, the proposed fractional positive- and negative-order moments? estimator, given in terms of two confluent hypergeometric functions, is solved numerically to yield the shape parameter. Using single pulse and multiple pulses in various clutter plus noise scenarios, we show, via both simulated and IPIX real data, a comparison of the new estimator with the estimators based on the higher-order moments, the fractional positive-order moments, the [zlog(z)], and the constrained maximum likelihood. [ABSTRACT FROM PUBLISHER]

We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In G. Pailloux et al. “Persymmetric adaptive radar detectors,” (2011) the probability of false alarm of the PS-AMF has been obtained in terms of the Gaussian hypergeometric function. In this paper, finite-sum expressions for the probability of false alarm of the PS-AMF are derived, which are more convenient to use in calculating the detection threshold. Moreover, the detection probabilities of the PS-AMF for both nonfluctuating and fluctuating target models are derived. In the fluctuating model, the amplitude of the target echoes is described by a generalized Chi distribution that involves the Rayleigh distribution as a special case. These theoretical results are all confirmed using Monte Carlo (MC) simulations. [ABSTRACT FROM PUBLISHER]

The estimation of the K-distributed radar clutter plus thermal noise parameters can be improved by employing fractional moments instead of the existing higher order moments. In order to achieve this, we provide first an asymptotic expression of the moments in terms of the confluent and Gauss hypergeometric functions. For fractional orders of the latter, the new estimation method is derived so that the estimates of the shape parameter are achieved via numerical computations of nonlinear equations. For lower fractional orders of moments and different values of the parameters, the numerical simulations demonstrate the effectiveness of the proposed estimation procedure when compared with the standard higher order moments method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The recent paper by Shnidman [1] introduced two new models for radar target fluctuation. One of them is the noncentral gamma-gamma (NCGG) distribution, specified by a density function expressed as an infinite sum. It is shown here that one can actually derive an expression for the density in terms of the confluent hypergeometric function. A general elementary expression is provided for the associated moments. [ABSTRACT FROM AUTHOR]