Topic
K-distribution
About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.
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Abstract: We examine the radar signatures and changes in the surface roughness associated with oceanic features in the low grazing angle (LGA) scattering regime. The X band (HH) radar signatures consist of high-amplitude sea spikes, step changes in the normalized radar cross-section (NRCS) modulations, and bright narrowbanded frontal structures. Using in situ observations coupled with airborne precision radiation thermometer (PRT-5) data, we show that the step changes in radar cross-section modulations are associated with either thermal stability-induced stress variations or current velocity variations. Superimposed on the step changes are additional modulations that result from wave breaking and hydrodynamic straining. The amplitudes of the NRCS LGA measurements are compared with the predictions of four backscattering models: the Bragg, the tilted-Bragg, the wedge, and the plume model. It is shown that while the simple Bragg model can describe the measurements to a limited degree, it generally tends to underpredict the results. Agreement is improved by including the tilt contribution from the longwave surface waves in the context of the composite scattering model. We use the wedge and plume models as the basis for explaining the cross sections associated with the high-amplitude sea spikes. The wedge model is used to describe scattering from sharply crested waves, and the plume model is used to describe the extreme cross sections that are associated with breaking waves near the fronts. In describing the probability density function characteristics we show that the backscattering statistics exhibit “K distribution” behavior for the Gulf Stream current region and near-frontal regions, while the cooler shelf waters have characteristics of an exponential distribution.
15 citations
23 May 1994
TL;DR: A construction of small probability spaces approximating general independent distributions, which is of smaller size than the constructions of [13] and can be efficiently combined with the method of conditional probabilities to yield improved NC algorithms for many problems.
Abstract: We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced in [21]. This construction can be efficiently combined with the method of conditional probabilities to yield improved NC algorithms for many problems such as set cover, set discrepancy, finding large cuts in graphs etc. The second is a construction of small probability spaces approximating general independent distributions, which is of smaller size than the constructions of [13].
15 citations
TL;DR: The dispersion function, defined as D(u) = E ∣ X − u ∣, characterizes the distribution function and gives a dispersive ordering of probability distributions that presents interesting properties as discussed by the authors.
15 citations
TL;DR: In this paper, the extreme points of certain compact convex sets of probability measures are determined, and this information is then used to obtain a representation of the characteristic functions of the probability distributions in those classes, in the same manner as Urbanik has proceeded for the class $L$.
Abstract: The subclasses of class $L$ probability distributions recently studied by K. Urbanik are characterized by requiring that certain functions be convex and have derivatives of some fixed order. The extreme points of certain compact convex sets of probability measures are determined, and this information is then used to obtain a representation of the characteristic functions of the probability distributions in those classes, in the same manner as Urbanik has proceeded for the class $L$.
15 citations
TL;DR: In this paper, higher-order coefficients (up to eighth order) were used to compare model distributions (K, log-normal, Rice-Nakagami and a universal statistical model distribution) with the measured data of 0.6328-μm laser-irradiance fluctuations propagated through a laboratory-simulated turbulence.
Abstract: Higher-order coefficients (up to eighth order) that give information concerning the contribution from the tails of non-Gaussian distributions are utilized to compare model distributions (K, log-normal, Rice–Nakagami, and a universal statistical model distribution) with the measured data of 0.6328-μm laser-irradiance fluctuations propagated through a laboratory-simulated turbulence. Expressions for the coefficients of the K distribution are derived in this paper. The comparison is made using a plot of the coefficients as a function of σ1 = (1.23Cn2k7/6L11/6)1/2, Cn2 being the refractive-index structure constant. Different values of the exponent α (or p) in Γn ~ σ1α (or Γn ~ Lp) computed from the measured data show that the exponent parameters increase with the order of n of the coefficients Γn. The parameters α or p can be useful for accurate modeling of the complex process.
15 citations