The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

G Peltzer - One of the best experts on this subject based on the ideXlab platform.

  • systematic insar tropospheric Phase Delay corrections from global meteorological reanalysis data
    Geophysical Research Letters, 2011
    Co-Authors: R Jolivet, Raphael Grandin, Cecile Lasserre, M P Doin, G Peltzer
    Abstract:

    [1] Despite remarkable successes achieved by Differential InSAR, estimations of low tectonic strain rates remain challenging in areas where deformation and topography are correlated, mainly because of the topography-related atmospheric Phase screen (APS). In areas of high relief, empirical removal of the stratified component of the APS may lead to biased estimations of tectonic deformation rates. Here we describe a method to correct interferograms from the effects of the spatial and temporal variations in tropospheric stratification by computing tropospheric Delay maps coincident with SAR acquisitions using the ERA-Interim global meteorological model. The modeled Phase Delay is integrated along vertical profiles at the ERA-I grid nodes and interpolated at the spatial sampling of the interferograms above the elevation of each image pixel. This approach is validated on unwrapped interferograms. We show that the removal of the atmospheric signal before Phase unwrapping reduces the risk of unwrapping errors in areas of rough topography.

Hatim F Alqadah - One of the best experts on this subject based on the ideXlab platform.

  • qualitative inverse scattering for sparse aperture data collections using a Phase Delay frequency variation constraint
    IEEE Transactions on Antennas and Propagation, 2020
    Co-Authors: Matthew J Burfeindt, Hatim F Alqadah
    Abstract:

    We present a formulation of the linear sampling method (LSM) for reconstructing target shape from spatially sparse data sets. The technique compensates for a lack of spatial data by incorporating $a$ priori propagation information into the LSM inversion. A constraint is placed on the LSM solution that enforces a Phase relationship across frequency that is determined by the electrical path length between the array transmitters and the imaging scene pixels. The effect of the constraint is that image artifacts that do not evince the expected Phase relationship according to their position relative to the array are suppressed. We apply the proposed technique to simulated and experimental sparse-aperture data sets and show that the resulting imagery is significantly more faithful to the true target shape as compared to the imagery created with the standard LSM.

  • sparse aperture qualitative inverse scattering using a Phase Delay based frequency variation constraint
    2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID), 2019
    Co-Authors: Matthew J Burfeindt, Hatim F Alqadah
    Abstract:

    The Linear Sampling Method (LSM) is a qualitative inverse scattering technique for reconstructing target support. The LSM has various beneficial qualities, including low computational expense and robustness to model error. However, it has a well-known limitation in that it requires dense, multiview, multistatic data. In this paper, we introduce a technique to improve multifrequency LSM imaging in sparse aperture scenarios by incorporating a priori knowledge of propagation-based Phase Delay into the LSM inversion. We apply the proposed technique to simulated data, and show that the Phase-Delay-based constraint leads to higher image fidelity in the form of significantly fewer imaging artifacts as compared to the conventional multifrequency LSM implementation, at the expense of a modest blurring of the imaged target boundaries in some cases due to imperfections in the Phase assumptions for resonant target features.

Qian Wang - One of the best experts on this subject based on the ideXlab platform.

  • permanent magnet synchronous motor sensorless control using proportional integral linear observer with virtual variables a comparative study with a sliding mode observer
    Energies, 2019
    Co-Authors: Baochao Wang, Yangrui Wang, Liguo Feng, Shanlin Jiang, Qian Wang
    Abstract:

    Quick convergence, simple implementation, and accurate estimation are essential features of realizing permanent-magnet synchronous motor (PMSM) position estimation for sensorless control using microcontrollers. A linear observer is often designed on real plant variables and is more sensitive to parameter uncertainty/variations. Thus, conventionally, a sliding mode observer (SMO)-based technique is widely used for its simplicity and convergence ability against parameter uncertainty. Although SMO has been improved for switching chattering and Phase Delay, it provides purely proportional gain, which leads to steady-state error and chattering in observation results. Different from conventional linear observer using real plant variables or SMO with proportional gain, a simple proportional-integral linear observer (PILO) using virtual variables is proposed in this paper. This paper also provides a comparative study with SMO. By introducing virtual variables without physical meaning, the PILO is able to simplify observer relations, get smaller Phase shifts, adapt mismatched parameters, and obtain a fixed Phase-shift relation. The PILO is not only simple, but also improves the estimation precision by solving the controversy between chattering and Phase-Delay, steady-state error. Moreover, the PILO is less sensitive to parameters mismatching. Simulation and experimental results indicate the merits of the PILO technique.

Christopher J Wretman - One of the best experts on this subject based on the ideXlab platform.

Pedro M A Miranda - One of the best experts on this subject based on the ideXlab platform.

  • on the use of the wrf model to mitigate tropospheric Phase Delay effects in sar interferograms
    IEEE Transactions on Geoscience and Remote Sensing, 2011
    Co-Authors: Giovanni Nico, Ricardo Tome, Joao P S Catalao, Pedro M A Miranda
    Abstract:

    A method that is used to generate synthetic interferograms of the atmospheric Phase Delay temporal changes is presented. The Weather Research and Forecasting Model is used to forecast the spatial distribution of the main atmospheric parameters at the acquisition times of synthetic aperture radar (SAR) images. The method is applied to mitigate atmospheric artifacts in SAR interferograms. The Lisbon Region and the Pico and Faial Islands in the Azores archipelago are chosen as case studies. They are characterized by a different temporal behavior of atmospheric Phase Delay properties. Results are assessed by means of a statistical analysis.