K.D. Ward

Bio: K.D. Ward is an academic researcher. The author has contributed to research in topics: K-distribution & Clutter. The author has an hindex of 1, co-authored 1 publications receiving 378 citations.

Compound representation of high resolution sea clutter

Abstract: A proposed form of compound distribution to describe the non-Rayleigh distribution and correlation properties of high resolution radar sea clutter is shown to be a good fit to experimental data. From this model the K distribution is derived, and a possible physical mechanism is discussed.

Sea Clutter: Scattering, the K Distribution and Radar Performance

Abstract: Sea Clutter: Scattering, the K Distribution and Radar Performance, 2nd Edition gives an authoritative account of our current understanding of radar sea clutter. Topics covered include the characteristics of radar sea clutter, modelling radar scattering by the ocean surface, statistical models of sea clutter, the simulation of clutter and other random processes, detection of small targets in sea clutter, imaging ocean surface features, radar detection performance calculations, CFAR detection, and the specification and measurement of radar performance. The calculation of the performance of practical radar systems is presented in sufficient detail for the reader to be able to tackle related problems with confidence. In this second edition the contents have been fully updated and reorganised to give better access to the different types of material in the book. Extensive new material has been added on the Doppler characteristics of sea clutter and detection processing; bistatic sea clutter measurements; electromagnetic scattering theory of littoral sea clutter and bistatic sea clutter; the use of models for predicting radar performance; and use of the K distribution in other fields.

A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis

Abstract: In this paper, we present a new similarity measure for automatic change detection in multitemporal synthetic aperture radar images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated by using a cumulant-based series expansion, which approximates probability density functions in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given, allowing a simple computation which depends on the four first statistical moments of the pixels inside the analysis window only. The proposed change indicator is compared to the classical mean ratio detector and also to other model-based approaches. Tests on the simulated and real data show that our detector outperforms all the others. The fast computation of the proposed detector allows a multiscale approach in the change detection for operational use. The so-called multiscale change profile (MCP) is introduced to yield change information on a wide range of scales and to better characterize the appropriate scale. Two simple yet useful examples of applications show that the MCP allows the design of change indicators, which provide better results than a monoscale analysis

Radar Detection Prediction in K-Distributed Sea Clutter and Thermal Noise

Abstract: The compound K-distribution model for high resolution seaclutter is extended to cover the addition of thermal noise. Thismodel is not only a good match to real data but also allows thepulse-to-pulse correlation of the clutter returns to be modeled. Thematching of real data and the analysis required for target detectionprediction are described, together with some typical detectionresults.

Generalized K distribution: a statistical model for weak scattering

Abstract: The generalized K distribution, introduced recently by Barakat [ J. Opt. Soc. Am. A3, 401 ( 1986)] as a model for the intensity of radiation scattered with a nonuniform distribution of phase by a fluctuating population of objects, is derived on the bases of simpler and more general arguments. In particular, Barakat’s analysis is based on a random walk in two dimensions and a specific model for the phase distribution; our analysis is presented in terms of an n-dimensional walk with a directional bias that, apart from its scaling property as a function of the mean number of scatters, is arbitrary. The relationship of the generalized K model with possible underlying scattering mechanisms is also discussed.

Coherent detection of radar targets in a non-gaussian background

Abstract: The problem of detecting radar targets against a background of coherent, correlated, non-Gaussian clutter is studied with a two-step procedure. In the first step, the structure of the amplitude and the multivariate probability density functions (pdfs) describing the statistical properties of the clutter is derived. The starting point for this derivation is the basic scattering problem, and the statistics are obtained from an extension of the central limit theorem (CLT). This extension leads to modeling the clutter amplitude statistics by a mixture of Rayleigh distributions. The end product of the first step is a multidimensional pdf in the form of a Gaussian mixture, which is then used in step 2. The aim of step 2 is to derive both the optimal and a suboptimal detection structure for detecting radar targets in this type of clutter. Some performance results for the new detection processor are also given. >