Parameter Estimation of Micro-Motion Targets for High-Resolution-Range Radar Using Online Measured Reference

Micro-motion dynamics produce Micro-range (m-R) signatures which are important features for target classification and recognition, provided that the range resolution of radar signal is high enough. However, dechirping the echo with reference measured by narrow bandwidth radar would generate the residual translational motion, which exhibits as random shifts of envelopes of range profiles. The residual translational motion would destroy the periodicity of m-R signatures and make a challenge to estimate rotational parameter. In this work, we proposed an efficient high-resolution range profile (HRRP)-based method to estimate rotational parameter, in which online measured reference distances are used to dechirp the radar raw echo. Firstly, the deduction for the modified first conditional comment of range profiles (MFCMRP) is introduced in detail, and the MFCMRP contain periodic and random components when dechirped by measured reference, corresponding to the rotational motion and the reference measured errors compared with actual reference. Secondly, the Wavelet Transform (WT) is utilized to separate the measured errors from the MFCMRP. The estimations of measured errors are used to compensate the MFCMRP, and then autocorrelation is performed on the estimated periodic component to obtain the estimation of rotational period. Lastly, the rotational amplitudes and phases are achieved by inverse Radon transform (IRT) of the compensated HRRP. The effectiveness of the proposed method in this paper is verified by synthetic data and measured radar data.

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High-Resolution Coherency Functionals for Improving the Velocity Analysis of Ground-Penetrating Radar Data

Remote Sensing ◽

10.3390/rs12132146 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2146

Author(s):

Eusebio Stucchi ◽

Adriano Ribolini ◽

Andrea Tognarelli

Keyword(s):

High Resolution ◽

Velocity Field ◽

Ground Penetrating Radar ◽

Energy Content ◽

Signal To Noise Ratio ◽

Synthetic Data ◽

Radar Data ◽

Position Type ◽

Reliable Interpretation ◽

Ground Penetrating

We aim at verifying whether the use of high-resolution coherency functionals could improve the signal-to-noise ratio (S/N) of Ground-Penetrating Radar data by introducing a variable and precisely picked velocity field in the migration process. After carrying out tests on synthetic data to schematically simulate the problem, assessing the types of functionals most suitable for GPR data analysis, we estimated a varying velocity field relative to a real dataset. This dataset was acquired in an archaeological area where an excavation after a GPR survey made it possible to define the position, type, and composition of the detected targets. Two functionals, the Complex Matched Coherency Measure and the Complex Matched Analysis, turned out to be effective in computing coherency maps characterized by high-resolution and strong noise rejection, where velocity picking can be done with high precision. By using the 2D velocity field thus obtained, migration algorithms performed better than in the case of constant or 1D velocity field, with satisfactory collapsing of the diffracted events and moving of the reflected energy in the correct position. The varying velocity field was estimated on different lines and used to migrate all the GPR profiles composing the survey covering the entire archaeological area. The time slices built with the migrated profiles resulted in a higher S/N than those obtained from non-migrated or migrated at constant velocity GPR profiles. The improvements are inherent to the resolution, continuity, and energy content of linear reflective areas. On the basis of our experience, we can state that the use of high-resolution coherency functionals leads to migrated GPR profiles with a high-grade of hyperbolas focusing. These profiles favor better imaging of the targets of interest, thereby allowing for a more reliable interpretation.

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Application of Time-Domain Full Waveform Inversion to Cross-Hole Radar Data Measured at Xiuyan Jade Mine, China

Sensors ◽

10.3390/s18093114 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3114 ◽

Cited By ~ 7

Author(s):

Sixin Liu ◽

Xintong Liu ◽

Xu Meng ◽

Lei Fu ◽

Qi Lu ◽

...

Keyword(s):

High Resolution ◽

Ray Tracing ◽

Time Domain ◽

Waveform Inversion ◽

Synthetic Data ◽

Radar Data ◽

Liaoning Province ◽

Full Waveform Inversion ◽

Full Waveform ◽

The Time Domain

Xiuyan Jade, produced in Xiuyan County, Liaoning Province, China is one of the four famous jade in China. King Jade, which is deemed the largest jade body of the world, was broken out from a hill. The local government planned to build a tourism site based on the jade culture there. The purpose of the investigation was to evaluate the stability of subsurface foundation, and the possible positions of mined-out zones to prevent the further rolling of the jade body. Cross-hole radar tomography is the key technique in the investigation. Conventional travel time and attenuation tomography based on ray tracing theory cannot provide high-resolution images because only a fraction of the measured information is used in the inversion. Full-waveform inversion (FWI) can provide high-resolution permittivity and conductivity images because it utilizes all the information provided by the radar signals. We deduce the gradient expression of the time-domain FWI with respect to the permittivity and conductivity using a method that is different from that of the previous work and realize the FWI algorithm that can simultaneously update the permittivity and conductivity by using the conjugate gradient method. Inverted results from synthetic data show that time-domain FWI can significantly improve the resolution compared with the ray-based tomogram methods. FWI can distinguish targets that are as small as one-half to one-third wavelength and the inverted physical values are closer to the real ones than those provided by the ray tracing method. We use the FWI algorithm to the field data measured at Xiuyan jade mine. Both the inverted permittivity and conductivity can comparably delineate four mined-out zones, which exhibit low-permittivity and low-conductivity characteristics. Furthermore, the locations of the interpreted mined-out zones are in good agreement with the existing mining channels recorded by geological data.

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An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

Monthly Weather Review ◽

10.1175/2007mwr2120.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 945-963 ◽

Cited By ~ 31

Author(s):

Jidong Gao ◽

Ming Xue

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Radial Velocity ◽

Computational Cost ◽

Radar Data ◽

Horizontal Resolution ◽

Velocity Data ◽

Background Error ◽

Cross Covariance ◽

Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

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Synthetic data generation of high-resolution hyperspectral data using DIRSIG

10.1117/12.735264 ◽

2007 ◽

Cited By ~ 2

Author(s):

Marek K. Jakubowski ◽

David Pogorzala ◽

Timothy J. Hattenberger ◽

Scott D. Brown ◽

John R. Schott

Keyword(s):

High Resolution ◽

Synthetic Data ◽

Hyperspectral Data ◽

Data Generation ◽

Synthetic Data Generation

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New LWD Image Improvement Method to Mitigate Interpretation Risks

10.2118/205222-ms ◽

2021 ◽

Author(s):

Tianhua Zhang ◽

Shiduo Yang ◽

Chandramani Shrivastava ◽

Adrian A ◽

Nadege Bize-Forest

Keyword(s):

High Resolution ◽

Real Time ◽

Best Practice ◽

Synthetic Data ◽

Image Resolution ◽

Drilling Process ◽

Drilling Tool ◽

Stick Slip ◽

Geological Interpretation ◽

Wellbore Temperature

Abstract With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling). At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images. In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections. Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.

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A Microphysical Bulk Formulation Based on Scaling Normalization of the Particle Size Distribution. Part I: Description

Journal of the Atmospheric Sciences ◽

10.1175/jas3620.1 ◽

2005 ◽

Vol 62 (12) ◽

pp. 4206-4221 ◽

Cited By ~ 27

Author(s):

Wanda Szyrmer ◽

Stéphane Laroche ◽

Isztar Zawadzki

Keyword(s):

Particle Size ◽

High Resolution ◽

Particle Size Distribution ◽

Size Distribution ◽

Numerical Models ◽

Radar Data ◽

Retrieval Algorithm ◽

Microphysical Processes ◽

The Moment ◽

Microphysical Parameterization

Abstract The authors address the problem of optimization of the microphysical information extracted from a simulation system composed of high-resolution numerical models and multiparameter radar data or other available measurements. As a tool in the exploration of this question, a bulk microphysical scheme based on the general approach of scaling normalization of particle size distribution (PSD) is proposed. This approach does not rely on a particular functional form imposed on the PSD and naturally leads to power-law relationships between the PSD moments providing an accurate and compact PSD representation. To take into account the possible evolution of the shape/curvature of the distribution, ignored within standard one- and two-moment microphysical schemes, a new three-moment scheme based on the two-moment scaling normalization is proposed. The methodology of the moment retrieval included in the three-moment scheme can also be useful as a retrieval algorithm combining different remote sensing observations. The developed bulk microphysical scheme presents a unified formulation for microphysical parameterization using one, two, or three independent moments, suitable in the context of data assimilation. The effectiveness of the scheme with different combinations of independent moments is evaluated by comparison with a very high resolution spectral model within a 1D framework on representative microphysical processes: rain sedimentation and evaporation.

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Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-13-0107.1 ◽

2014 ◽

Vol 71 (4) ◽

pp. 1353-1370 ◽

Cited By ~ 3

Author(s):

Sabrina Gentile ◽

Rossella Ferretti ◽

Frank Silvio Marzano

Keyword(s):

High Resolution ◽

Conceptual Model ◽

Vertical Structure ◽

Mesoscale Model ◽

Tropical Rainfall Measuring Mission ◽

Sea Breeze ◽

Radar Data ◽

Full Life Cycle ◽

Resolution Model ◽

High Resolution Model

Abstract One event of a tropical thunderstorm typically observed in northern Australia, known as Hector, is investigated using high-resolution model output from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) observations from a ground-based weather radar located in Berrimah (Australia) and data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The analysis is carried out by tracking the full life cycle of Hector from prestorm stage to the decaying stage. In both the prestorm stage, characterized by nonprecipitating cells, and the triggering stage, when the Hector storm is effectively initiated, an analysis is performed with the aid of high-spatial-and-temporal-resolution MM5 output and the Berrimah ground-based radar imagery. During the mature (“old”) stage of Hector, considering the conceptual model for tropical convection suggested by R. Houze, TRMM Microwave Imager satellite-based data were added to ground-based radar data to analyze the storm vertical structure (dynamics, thermodynamics, and hydrometeor contents). Model evaluation with respect to observations (radar reflectivity and TRMM data) suggests that MM5 performed fairly well in reproducing the dynamics of Hector, providing support to the assertion that the strength of convection, in terms of vertical velocity, largely contributes to the vertical distribution of hydrometeors. Moreover, the stages of the storm and its vertical structure display good agreement with Houze’s aforementioned conceptual model. Finally, it was found that the most important triggering mechanisms for this Hector event are topography, the sea breeze, and a gust front produced by previous convection.

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Bayesian Classifier for Sparsity-Promoting Feature Selection

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415500226 ◽

2015 ◽

Vol 29 (06) ◽

pp. 1550022 ◽

Cited By ~ 1

Author(s):

Danlei Xu ◽

Lan Du ◽

Hongwei Liu ◽

Penghui Wang

Keyword(s):

Feature Selection ◽

Synthetic Data ◽

Original Data ◽

Radar Data ◽

Bayesian Classifier ◽

Classification Model ◽

Data Sets ◽

Data Set ◽

Classification Boundary ◽

Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

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