Showing papers on "K-distribution published in 2018"

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

Abstract: Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

Performance of Non-Parametric CFAR Detectors in Log-Normal and K Radar clutter

Abstract: In this work, the performance of logt-, GMOS-(Geometric Mean Order Statistic), TMOS- (Trimmed MOS) and IE-CFAR (Inclusion/Exclusion) detectors are investigated in presence of log-normal and K distributed clutter. First, for a finite number of clutter samples, dependence of the false alarm probability $P_{FA}$ upon clutter parameters is examined. The CFAR property for the case of log-normal clutter is maintained while the $P_{FA}$ depends somewhat on the shape parameter of the K distribution. Then, by carrying out Monte-Carlo simulations, we show that for the case of log-normal clutter a small detection difference exists between the underlying CFAR detectors. In the case of K-distributed clutter, there is a significant detection difference for small values of the shape parameter (spiky clutter case).

Shape Parameter Estimation for K-Distribution Using Variational Bayesian Approach

Abstract: The sea clutter component in some of the radar and sonar signal models can be statistically characterized as following a K-distribution. This distribution has a shape parameter that is directly related to the number of scatterers. Hence, the estimation of this shape parameter is an important problem and is traditionally addressed using the maximum likelihood (ML), the method of moments (MoM) and their variants. A shortcoming of these methods is lesser accuracy in comparison to the theoretical CRB. In this paper, a variational Bayesian algorithm is proposed that provides both improved convergence and superior accuracy in comparison to the existing algorithms.

New approximation for pdf of K- distribution: analytical study of QoS parameters in free space optical communication

Abstract: A new approximate expression for the probability density function (pdf) of K-distribution is proposed. The approximate pdf is found to be in good agreement with the exact analytical closed-form expression over the desired range of scintillation index lying between 2 and 3. Employing the proposed pdf, the expression in respect of two of the QoS measures of fade probability and bit error rate (BER) valid under various modulation schemes is given. Using the generalised expression of BER, plots under ON–OFF keying and binary phase shift keying modulation schemes are shown. The resulting expressions provide a better understanding in contrast to the exact results obtained in terms of Meijer G functions. Numerical computations are carried out to demonstrate the efficacy of approximate results.

Analysis of Experimental Data of IPIX Radar

Abstract: Doppler spectrum and amplitude statistics are the effective means to describe and analyze the sea clutter. The research on the law of the sea clutter is important for the improvement of radar detection performance and the acquisition of ocean information. This paper makes a study on the IPIX radar experiment data in the case of X-band electromagnetic frequency and low glancing angle, we compared the impacts of different levels of the sea, the wind and polarization on the Doppler spectrum and amplitude statistical distribution characteristics of sea clutter. On the other hand, we fit the distribution of sea clutter with Rayleigh distribution, lognormal distribution, Weibull distribution, k distribution and Tsallis distribution to research the optimal distribution characteristics.

A Numerical Integral Algorithm Based on the CAPSO to Improve the Estimation for the Parameters of the Homodyned-K Distribution

Abstract: The homodyned-K (HK) distribution is a widely used statistical model, whose parameters have different physical-meanings for tissue characterization. In the present study, the maximum likelihood estimation (MLE) method based on the Newton-Raphson algorithm is proposed to estimate the HK parameters solely. For improving the accuracy and convergence of the MLE, the cloud adaptive particle swarm optimization (CAPSO) algorithm is proposed for the integral calculation of the probability density function (PDF) of the HK distribution. In the experiments, sets of samples satisfying the HK distribution are generated, and then the parameters are estimated by the proposed CAPSO-based MLE method. The statistics of estimation errors are calculated, and compared with the results based on the mean intensity and X- and U-statistics (XU) method, which is the latest one based on moment estimation. Experimental results show that the proposed method can solely estimate the HK parameters with a small error level, which means a further practical value in ultrasonic applications.

A study of geometric and statistical curvatures for the skew-normal family of distributions

Abstract: With special reference to the family of skew-normal distributions, we consider geometric curvature of a probability density function as a means to define and identify rare or catastrophic events—a phenomenon common in studying the financial instruments Further, we study the statistical curvature properties of this family of distributions and discuss the sample size issue, to assess, to what extent the linear and likelihood-based inference of exponential family of distribution can be applicable for the skew-normal family

The generalized exponential family of distributions and its characteristics

Abstract: In this paper we generalize the exponential family (EF) of distributions into a wider family which includes important distributions such as the normal, log-normal, Student-t, Cauchy, logistic and Birnbaum–Saunders distributions. Furthermore, we derive several characteristics of the proposed family. The importance of such family is also discussed.

Robust Non Parametric CFAR Detector in Compound Gaussian Clutter in the Presence of Thermal Noise and Interfering Targets

Abstract: The Concept of constant false alarm rate CFAR detection is usually a requirement for any modern radar system. This paper proposes a generalization of a robust CFAR detector to account for the presence of thermal noise and interfering targets. We show via simulation results that the proposed detector keeps the CFAR property for a class of compound Gaussian clutter, namely: the K distribution, the Generalized Pareto distribution and the Compound Inverse Gaussian distribution. The results obtained show that the probability of false alarm is almost independent of the clutter parameter for all the cases studied.

Maximum Entropy Beyond Selecting Probability Distributions

Abstract: Traditionally, the Maximum Entropy technique is used to select a probability distribution in situations when several different probability distributions are consistent with our knowledge. In this paper, we show that this technique can be extended beyond selecting probability distributions, to explain facts, numerical values, and even types of functional dependence.

Performance Analysis of Energy Detection Based Cognitive Radio over GBK Fading Channel

Abstract: A Generalised Bessel K fading distribution (GBK) has flexible parameters and own the capability to capture the multipath as well as shadowing effect. Moreover, it reduces to several well known multipath fading cases such as exponential distribution, generalised K distribution, generalised Gamma distribution, Half Gaussian, K distribution, Gamma distribution. In this work, our goal is to obtain analytical expression of average probability of detection (APD) in case of GBK fading channel and then to carry out the system performance analysis by applying square law selection (SLS) diversity scheme. Effect of different shaping parameters on system performance is also examined.

Application of a New Distribution to High Grazing Angle Sea-Clutter

Abstract: Many studies show that the sea clutter obeys the K distribution With the continuous improvement of the radar resolution and the increase of the grazing angle, the distribution of sea clutter gradually deviates from the K distribution Although good fitting results can be obtained through the more complicated sea clutter models such as KK distribution, KA distribution, K+Rayleigh distribution and so on, the computational efficiency of the detection algorithm is reduced obviously For computational complexities are caused by large number of parameter estimations and non-closed expression A hybrid sea-clutter distribution (KR distribution) with closed-form expression for high-resolution and high grazing angle situation is proposed, which fits the measured data The method of parameter estimation for KR distribution is given, that is, the geometric segmentation parameter estimation method based on path gain The results show that the fitting effect of KR distribution is better than both of K distribution and Rayleigh distribution under the conditions of high resolution and large grazing angle The fitting effect will be better as the grazing angle increases The parameter estimation method of geometric segmentation based on path gain can find the fitting parameters accurately

Simulation of non-stationary sea clutter based on DSP

Abstract: Simulation of the sea clutter signal by using digital signal processing (DSP) platform for radar performance testing is an prospective and widely used technique in radar engineering application. Based on the digital signal processor TMS320C6748 as the hardware platform and the autoregressive model, a method of generating non-stationary K distributed sea clutter is proposed. In DSP realization of the non-stationary sea clutter sequence simulation, its amplitude usually satisfies the K distribution, and the amplitude envelope and Doppler spectral center frequency will change with time. The simulation results show that the probability distribution and time-frequency characteristics of the sea clutter are in good agreement with the theoretical values.