Matsuo Sekine

Bio: Matsuo Sekine is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Clutter & Weibull distribution. The author has an hindex of 16, co-authored 82 publications receiving 896 citations.

Weibull radar clutter

Abstract: The material presented in this book is intended to provide the reader with a pratical treatment of Weibull distribution as applied to radar systems. This book is primarily written for radar engineeres. Topics include: general derivation of Weibull distribution, measurements of Weibull-distributed clutter, comparison of Weibulkl distribution with various distributions including Rayleigh, gamma, log-nornal and k- distributions to name just a few.

Weibull-Distributed Ground Clutter

Abstract: Weibull-distributed ground clutter of cultivated land was measured using an L-band long-range air-route surveillance radar (ARSR) having a 3.0 ?s pulsewidth and a 1.23° beamwidth at very low grazing angles between 0.21° and 0.32°. It is shown that the shape parameter of the Weibull distribution varied from c = 1.507 to c = 2.0, corresponding to the Rayleigh distribution.

Weibull raindrop-size distribution and its application to rain attenuation

Abstract: In the design of radio systems such as terrestrial and satellite communication links at frequencies above 10 GHz, rain attenuation is an important problem. A Weibull raindrop-size distribution is proposed by fitting the measurements of rainfall observed using a distrometer in Tokyo. A propagation experiment at 103 GHz is introduced. Rain attenuation coefficients are calculated by considering the Mie scattering using this Weibull distribution for raindrop size and are compared with some experimental data. The results of frequency characteristics from this Weibull raindrop-size distribution agrees well with some experimental data for the range 8–312.5 GHz.

Rain Attenuation 0f Centimeter, Millimeter and Submillimeter Radio Waves

Abstract: The rain attenuation from 1 to 1000 GHz was calculated by using a Weibu11 distribution for raindrop-size, which was assumed to be caused by coalescence, drop break-up and a chain reaction process. The results showed a good agreement with the recent microwave measurements from 8 to 312.5 GHz.

On Weibull-Distributed Weather Clutter

Abstract: Weather clutter was observed using an L band range (200nmi) air-route surveillance radar (ARSR). It is shown that the measured clutter amplitudes obey a Weibull distribution.

Asymptotically optimum radar detection in compound-Gaussian clutter

Abstract: An asymptotically optimum receiver designed for detecting coherent pulse trains in compound-Gaussian clutter is introduced and assessed. The proposed receiver assumes knowledge of the structure of the clutter covariance matrix, but does not require that of its amplitude probability density function (apdf). Performance is analytically evaluated, showing that the loss, as measured with respect to the corresponding optimum structure, is kept within a few dBs even for a relatively small number of integrated pulses and that it largely outperforms the matched-filter detector under all instances of practical interest. Interestingly, the proposed detector achieves constant false alarm rate (CFAR), regardless of the clutter envelope distribution and, consequently, its power. >

Complex Elliptically Symmetric Distributions: Survey, New Results and Applications

Abstract: Complex elliptically symmetric (CES) distributions have been widely used in various engineering applications for which non-Gaussian models are needed. In this overview, circular CES distributions are surveyed, some new results are derived and their applications e.g., in radar and array signal processing are discussed and illustrated with theoretical examples, simulations and analysis of real radar data. The maximum likelihood (ML) estimator of the scatter matrix parameter is derived and general conditions for its existence and uniqueness, and for convergence of the iterative fixed point algorithm are established. Specific ML-estimators for several CES distributions that are widely used in the signal processing literature are discussed in depth, including the complex t -distribution, K-distribution, the generalized Gaussian distribution and the closely related angular central Gaussian distribution. A generalization of ML-estimators, the M-estimators of the scatter matrix, are also discussed and asymptotic analysis is provided. Applications of CES distributions and the adaptive signal processors based on ML- and M-estimators of the scatter matrix are illustrated in radar detection problems and in array signal processing applications for Direction-of-Arrival (DOA) estimation and beamforming. Furthermore, experimental validation of the usefulness of CES distributions for modelling real radar data is given.

Despeckling of medical ultrasound images

Abstract: Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters - wavelet denoising, total variation filtering, and anisotropic diffusion - and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.