Search

In this paper, a functional model for determining the minimum and maximum detectable deformation gradient in terms of coherence for synthetic aperture radar (SAR) sensors is presented. The model is developed based on a new methodology that incorporates both real and simulated data. Sets of representative surface deformation models have been simulated, and the associated phase from these models introduced into real SAR data acquired by European Remote Sensing 1 and 2 satellites. Subsequently, interferograms were derived, and surface deformation was estimated. A number of cases of surface deformation with varying magnitudes and spatial extent have been simulated. In each case, the resultant surface deformation has been compared with the 'true' surface deformation as defined by the deformation model. Based on these comparisons, a set of observations that lead to a new functional model has been established. Finally, the proposed model has been validated against external datasets and proven viable. Although the major weakness of the model is its reliance on visual interpretation of interferograms, this model can serve as a decision-support tool to determine whether or not to apply satellite radar interferometry to study a given surface deformation. Index Terms--Coherence, deformation, European Remote Sensing (ERS) satellite, interferometric synthetic aperture radar (InSAR), radar interferometry.

We present a modification to the adaptive Goldstein radar interferogram filter which improves the quality of interferometry products. The proposed approach makes the Goldstein filter parameter alpha dependent on coherence, such that incoherent areas are filtered more than coherent areas. This modification minimizes loss of signal while still reducing the level of noise. Index Terms--Phase filtering, phase statistics, radar interferometry, synthetic aperture radar (SAR).

An experimental study was undertaken to verify an axisymmetric numerical model of a control valve flow field. The numerical model, which utilized Computational Fluid Dynamics (CFD), was formerly developed to be used as a design tool by manufacturers of control valves. In this work the model was first tested by comparing its results to data taken on an axisymmetric flow field experiment. Then the model's application to actual three-dimensional control valves was tested by studying the pressure and flow field through a three-dimensional control valve. The results showed that the axisymmetric numerical model is accurately modeling an axisymmetric flow field. In addition, the results showed that control valves have a predominantly axisymmetric flow field for most of their plug travel which make them suitable for the model. Finally, the results showed details about the flow field such as where separation and reattachment may occur. [DOI: 10.1115/1.1490126]

Computational Fluid Dynamics (CFD) tools are evaluated for use in industrial design applications by predicting primary control valve performance characteristics. The performance parameter of primary interest to the manufacturer is the flow coefficient, [C.sub.v]. Valves having relative valve capacity factors between 2.5 and 13 were modeled. The control valve [C.sub.v] was experimentally measured and numerically predicted. Both equal percentage and linear characteristic valves were represented in the study. The numerical (simulation) study presented in Part 1 showed that the valve [C.sub.v] and the inherent valve characteristic could be accurately predicted using axisymmetric flow models over most of the plug travel. In addition, the study demonstrates the usefulness of simplified CFD analysis for relatively complex 3-D flows. [DOI: 10.1115/1.1490108]

In global positioning system (GPS) data processing, incorrect stochastic models for double-differenced measurements will result in unreliable statistics for ambiguity search and biased positioning results. In the commonly used stochastic model, it is usually assumed that all the raw GPS measurements are independent and that they have the same variance. In fact, these assumptions are not realistic. Measurements obtained from different satellites cannot have the same accuracy due to varying noise levels. In this paper, a new method based on modern statistical theory is proposed to directly estimate the covariance matrix for double-differenced GPS measurements. Three different stochastic models have been tested and analyzed. Test results indicate that by using the proposed stochastic models the volume of ambiguity search space can be reduced and the reliability of the ambiguity resolution is improved. Also, the statistics of the baseline components estimated with the proposed stochastic models are more efficient.