• The Journal of the Acoustical Society of Korea
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• 제25권3E호
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• pp.115-122
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• 2006

The bistatic scattering of an incident wave by a hemi-spherically capped cylinder is of particular interest because it has rarely been studied until the present day. The configuration of a hemi-spherically capped cylinder is similar to naval underwater weapons (submarines, mines, torpedos, etc.), but which is not exactly the same. This paper describes a novel laboratory experiment aimed at direct measurement of bistatic scattering by a hemi-spherically capped cylinder. Bistatic scattering by a hemi-spherically capped cylinder was measured in an acoustic water tank (5m long, 5m wide, 5m deep) using a high frequency projector (120kHz) and hydrophone. Measurements of monostatic scattering were also made under the same conditions. The bistatic scattering pattern by a hemi-spherically capped cylinder was measured against the incident angles $(0^{\circ},\;15^{\circ},\;20^{\circ},\;30^{\circ},\;45^{\circ},\;60^{\circ},\;90^{\circ})$ in order to verify various scattering pattern characteristics by the change of incident angle. The results indicate that the bistatic scattering TS at a wide scattering angle is much stronger than the mono static scattering TS. In bistatic scattering, the forward scattering TS is significantly stronger than the backward scattering TS, and the forward scattering pattern is also broader. In case of seven incident angles, the maximum value of forward scattering TS is about 14dB stronger than that of backward scattering TS. It is also found that forward scattering varies with the incident angle of sound to a much less extent than backscattering, and it is not seriously affected by the incident angle. These features could be the advantages of using forward scattering for detecting underwater targets at long range and increasing detection area and probability.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.