Scattering Center Research Articles

Requirements and sensitivity analysis for temporally- and spatially-resolved thermometry using neutron resonance spectroscopy.

Neutron resonance spectroscopy (NRS) has been used extensively to make temperature measurements that are accurate, absolute, and nonperturbative within the interior of material samples under extreme conditions applied quasistatically. Yet NRS has seldom been used in dynamic experiments. There is a compelling incentive to do so because of the significant shortcomings of alternative techniques. An important barrier to adopting dynamic NRS thermometry is the difficulty in fielding it with conventional spallation neutron sources. To enable time-dependent and spatially resolved temperature measurements in dynamic environments, more compact neutron sources that can be used at user facilities in conjunction with other diagnostic probes (such as x-ray light sources) are required. Such sources may be available using ultrafast high-intensity optical lasers. We evaluate such possibilities by determining the sensitivities of the temperature estimate on neutron-beam and diagnostic parameters. Based on that evaluation, requirements are set on a pulsed neutron-source and diagnostics to make a meaningful dynamic temperature measurement. Dynamic thermometry measurements are examined in this context when driven by two alternative fast-neutron sources: the Los Alamos Neutron Scattering Center (LANSCE) proton accelerator driving isotropic spallation neutrons as a baseline and a laser-plasma ion accelerator driving a neutron beam from deuterium breakup. Strategies to close the gap between the required and demonstrated performance of laser-based fast-neutron sources are presented. A short-pulse high-intensity laser with state-of-the-art pulse contrast and an energy of a few hundred Joules would drive a compact neutron source suitable for NRS thermometry that could transform the dynamic study of materials.

Radar HRRP target recognition via statistics‐based scattering centre set registration

Due to the presence of noise and range resolution variation, each attribute of the scattering centre (SC) extracted from high-resolution range profile (HRRP) data is a random variable fluctuating around the real attribute. For the scatterer-based target recognition method, the extraction error will disturb the similarity measurement between SC sets and adversely affect the recognition performance. In this study, a novel approach for HRRP target recognition is proposed via the statistics-based SC set registration. The attribute set of SCs is first characterised by a Gaussian mixture model (GMM) in which each Gaussian component represents the corresponding SC attributes containing the amplitude and position. Besides, the noise power and range resolution are taken into account into the GMM parameters, which enhances the recognition robustness against noise and resolution variation. Afterwards, based on the different stability of SC attributes against the variation of target-sensor orientation, the weighted Gaussian quadratic form distance is developed to register the test Gaussian mixture with the templates. The resulting recognition method considers the statistical property of the estimated attribute of the SC and exhibits robust recognition performance. Experiments conducted on the measured data demonstrate the validity and robustness of the proposed method.

3D Fully Polarimetric Attributed Scattering Center Extraction Based on Sequential 2D SAR Images

Compared with the two-dimensional (2D) attributed scattering center model (ASCM), three dimensional (3D) fully polarimetric attributed scattering center model (FP-ASCM) is able to directly offer the target location, size, and orientation in 3D space, which plays an important role in noncooperative target recognition. Considering that 3D images of the target are hard to obtain, 3D fully polarimetric attributed scattering center feature extraction from sequential 2D SAR images is addressed, and a novel method is proposed based on sparse representation in a frequency-azimuth-elevation domain. The proposed method consists of two steps: scattering center associations from sequential images and 3D FP-ASCM parameter estimation. First, a new scattering center association method is proposed by using epipolar geometry and polarization characteristics. In this method, scattering centers from different images can be associated by minimizing their projection distance and polarimetric diversity. Second, 3D fully polarimetric attributed scattering center feature is extracted based on sparse representation in a frequency-azimuth-elevation domain. As 3D FP-ASCM includes eight target parameters and three radar parameters, the dimension of the parameterized dictionary is very large. Thus, parameter initialization and range-orientation uncoupling are adopted to reduce the dimension of the parameterized dictionary. The numerical results for electromagnetic computation data and measurement data show the efficiency of the proposed method.

Establishment of a Wideband Radar Scattering Center Model of a Plasma Sheath

A reentry object covered by a plasma sheath will produce two reflective signals in a radar echo, which seriously affects the detection of the reentry object by radar and results in positioning error and even tracking loss. On the basis of the scattering center model and the non-uniform plasma stratification model, the present paper establishes a radar scattering model of a reentry object covered by a plasma sheath. The model effectively retains the plasma distribution and electromagnetic wave propagation characteristics of the plasma sheath. Analysis of the signal components of radar echoes in an example shows to some extent that the non-mirror reflection signal is not generated by the plasma wake.

High Accuracy Scattering Center Modeling Based on PO and PTD

High Accuracy Scattering Center Modeling Based on PO and PTD

Target Reconstruction Based on 3-D Scattering Center Model for Robust SAR ATR

This paper proposes a robust synthetic aperture radar (SAR) automatic target recognition method based on the 3-D scattering center model. The 3-D scattering center model is established offline from the CAD model of the target using a forward method, which can efficiently predict the 2-D scattering centers as well as the scattering filed of the target at arbitrary poses. For the SAR images to be classified, the 2-D scattering centers are extracted based on the attributed scattering center model and matched with the predicted scattering center set using a neighbor matching algorithm. The selected model scattering centers are used to reconstruct an SAR image based on the 3-D scattering center model, which is compared with the test image to reach a robust similarity. The designed similarity measure comprehensively considers the image correlation between the test image and the model reconstructed image and the model redundancy as for describing the test image. As for target recognition, the model with the highest similarity is determined to the target type of the test SAR image when it is denied to be an outlier. Experiments are conducted on both the data simulated by an electromagnetic code and the data measured in the moving and stationary target acquisition recognition program under standard operating condition and various extended operating conditions to validate the effectiveness and robustness of the proposed method.

Compressive Sensing Based Multilevel Fast Multipole Acceleration for Fast Scattering Center Extraction and ISAR Imaging.

In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and process area as it utilizes the sparsity and measurement matrix to reconstruct the compressible signal from limited samples successfully. In this paper, CS is introduced into an efficient numerical method, multilevel fast multipole acceleration (MLFMA), for the electromagnetic (EM) scattering problem over a wide incident angle. This allows composition of a new kind of incident wave, which obtains efficient and reliable data for scattering centers extraction with low complexity. The resulting data from CS-based MLFMA are processed for ISAR) imaging. Simulation results show the received data for ISAR imaging from MLFMA with CS can outperform the data from MLFMA, which achieves a similar quality of ISAR imaging. Additionally, the computation complexity is improved by CS through the reduced matrix computation for fewer incident waves. It makes ISAR imaging using real data feasible and meaningful.

Physics design of the next-generation spallation neutron target-moderator-reflector-shield assembly at LANSCE

We discuss the physics design of the next-generation spallation neutron target-moderator-reflector-shield (TMRS) assembly for the Manuel Lujan Jr. Neutron Scattering Center (Lujan Center) at the Los Alamos Neutron Science Center (LANSCE). We developed this TMRS to improve the neutronic performance of the Lujan Center in the intermediate energy range (keV–MeV) enabling a variety of new nuclear physics experiments, as well as increasing the quality of the ongoing experiments. The results of our Monte Carlo optimization study indicate that the new TMRS will generate keV–MeV neutrons with higher intensity and improved time resolution. At the same time, the new design largely maintains its current performance in the cold and thermal ranges supporting materials research. In this article we show that the new TMRS has the potential to enhance the Lujan Center’s experimental capability in a wider interval of energies – cold to intermediate – after its scheduled installation in 2020.

A Region Matching Approach Based on 3-D Scattering Center Model With Application to SAR Target Recognition

This paper proposes a region matching approach based on 3-D scattering center model with application to synthetic aperture radar (SAR) automatic target recognition (ATR). Each of the model scattering centers is represented by a binary region obtained by segmenting its predicted image using a global threshold. Afterwards, a region-to-region matching algorithm is performed to match the individual predicted regions with the extracted target region from the test image. Three scores are designed to comprehensively evaluate the matching result, which are linearly fused as an overall similarity based on their sparabilities as for discriminating different targets. In the classification stage, a coarse-to-fine region matching is performed and the test image is decided to be the class with the maximum similarity. Experiments are conducted on the simulated data by the electromagnetic code. The results demonstrate that the proposed method outperforms several template-based and model-based SAR ATR methods under the standard operating condition. The superiority of the proposed method becomes more remarkable under various extended operating conditions, including noise corruption, resolution variance, and partial occlusion.

Target Discrimination for SAR ATR Based on Scattering Center Feature and K-center One-Class Classification

Target discrimination plays an important role in the typical synthetic aperture radar (SAR) automatic target recognition (ATR) system. The conventional discrimination features roughly describing the difference between the target and the clutter are useful to discriminate the target from natural clutter, but these features may not well discriminate the target from the artificial clutter. To solve the above problem, a new algorithm for target discrimination is proposed based on the scattering center feature and the K-center one-class classification in this paper. The amplitudes and locations of the scattering centers have not been used as discrimination feature to our best knowledge, even though the distributions of the scattering centers associated with the targets are different from those of the natural clutter and artificial clutter. Then, since the scattering center feature is a typical kind of point set feature, we improve the K-center one-class classification based on Hausdorff distance (HD), where HD is a distance measure for the point set feature. Experimental results based on the measured MiniSAR dataset show that the discrimination performance of the scattering center feature is better than that of the conventional discrimination features, especially for the artificial clutter.

Scattering center models of backscattering waves by dielectric spheroid objects.

Scattering center models provide a simple and effective way of describing the complex electromagnetic scattering phenomena of targets and have been successfully applied in radar applications. However, the existing models are limited to conducting objects. Numerical results show that scattering centers of dielectric objects are far more complex than conducting objects and most of them are distributed beyond the object. For the lossless and low-loss media, the major scattering contributions to total fields are surface waves and multiple internal reflections rather than the direct reflection. Concise scattering center models for backscattering from dielectric spheroid objects are proposed in this work, which can characterize the backscattered waves by scattering centers with sparse and physical parameters. Good agreement has been demonstrated between the high resolution range profiles simulated by this model with those obtained by Mie series and the full wave numerical method.

Fast Scattering Center Extraction With DDM Based on Compressive Sensing for ISAR Imaging

In this paper, we propose to employ compressive sensing (CS) in the domain decomposition method (DDM) to construct a new incident source with much fewer excitation wave to obtain the compressed-induced currents, which can then be recovered by orthogonal matching pursuit algorithm. The complexity of the computational electromagnetic (EM) model for solving the multiscale scattering problems is reduced significantly. Therefore, the received echo data can be much faster extracted from strong scattering centers of the target in EM environment, which can be utilized for ISAR imaging to achieve high resolution for moving targets. The new proposed CS-DDM algorithm requires much fewer sampling data to transmit, process, and storage, so that the accuracy and efficiency for ISAR imaging can be improved greatly, which can bring much benefit for the development of applications of security problems in smart campus.

Accurate Three-Dimensional Scattering Center Extraction for ISAR Image Using the Matched Filter-Based CLEAN Algorithm

Accurate Three-Dimensional Scattering Center Extraction for ISAR Image Using the Matched Filter-Based CLEAN Algorithm

Improvement in Computation Time of 3-D Scattering Center Extraction Using the Shooting and Bouncing Ray Technique

We present a fast 3-D scattering center extraction algorithm using the shooting and bouncing ray technique. The proposed algorithm generates a 3-D inverse synthetic aperture radar image from which a set of 3-D scattering centers is then extracted using the CLEAN algorithm. In the conventional extraction algorithm, computation time is improved using the fast Fourier transform (FFT)-based scheme. However, the memory requirement is greatly increased because of the high oversampling required to mitigate the interpolation error. Evaluation of memory-time tradeoffs indicates that the conventional algorithm is still too time consuming given the constraints of practical memory. Thus, a modified FFT-based scheme is proposed to improve computation time without increasing the memory requirement. Through modifying the ray spread function, the distortion from the interpolation error is mitigated without the use of high oversampling. Implementation based on the proposed FFT-based scheme accelerates the image formation process significantly. Numerical simulations of realistic targets are presented to demonstrate the performance of the proposed algorithm.

Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers

This paper presents an approach for attributed scattering center (ASC) matching with application to synthetic aperture radar (SAR) automatic target recognition (ATR). A statistics-based distance measure is designed to evaluate the distance between individual ASCs. Afterwards, the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets. Based on the correspondence, a global similarity and a local similarity are designed to comprehensively evaluate the global consistency and structural correlation between those two ASC sets. The two similarities comprehensively exploit the inner correlation between the two ASC sets, thus providing a reliable and robust similarity measure for SAR ATR. The two similarities are then fused based on the Dempster–Shafer evidence theory to determine the target type by the maximum belief rule. Extensive experiments conducted on the moving and stationary target acquisition and recognition dataset and the comparison with several state-of-the-art methods demonstrate the validity and robustness of the proposed method.

Data Augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Target Recognition

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target’s image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.

Attributed Scattering Center Extraction Algorithm Based on Sparse Representation With Dictionary Refinement

Compared with the point-scattering model, the attributed scattering center model (ASCM) is able to describe the frequency and aspect dependence of canonical scattering objects using solutions from both physical optics and the geometric theory of diffraction. As the ASCM is complicated, it may increase the dimension of the parameterized dictionary, which will increase the cost of computation and storage significantly. Aiming at this problem, a novel sparse representation-based algorithm, combined with an alternative optimization and dictionary refinement, is proposed. Utilizing the orthogonal matching pursuit algorithm combined with relaxation algorithm, the solution to the sparse signal recovery problem can be obtained. Numerical results on both electromagnetic computation data and measured SAR data verify the validity of the proposed algorithm.

High Precise Scattering Centers Models for Cone-Shaped Targets Based on Induced Currents

Based on induced currents of cone-shaped targets, the high precise scattering center model is derived in this paper. The distribution characteristics of the induced currents and their relationships with the attributes of scattering centers are investigated in detail; the high precise scattering center model is obtained. In order to validate the scattering center model, numerical simulations of two cone-shaped targets are presented, and the accuracies of the models are validated through comparing the range profiles simulated by the models with those by a full-wave numerical method. The validation results demonstrate that this model is superior to the existing model in precisely characterizing the scattering centers induced by creeping waves.

The Los Alamos Neutron Science Center Spallation Neutron Sources

The Los Alamos Neutron Science Center (LANSCE) provides the scientific community with intense sources of neutrons, which can be used to perform experiments supporting civilian and national security research. These measurements include nuclear physics experiments for the defense program, basic science, and the radiation effect programs. This paper focuses on the radiation effects program, which involves mostly accelerated testing of semiconductor parts.When cosmic rays strike the earth's atmosphere, they cause nuclear reactions with elements in the air and produce a wide range of energetic particles. Because neutrons are uncharged, they can reach aircraft altitudes and sea level. These neutrons are thought to be the most important threat to semiconductor devices and integrated circuits. The best way to determine the failure rate due to these neutrons is to measure the failure rate in a neutron source that has the same spectrum as those produced by cosmic rays.Los Alamos has a high-energy and a low-energy neutron source for semiconductor testing. Both are driven by the 800-MeV proton beam from the LANSCE accelerator. The high-energy neutron source at the Weapons Neutron Research (WNR) facility uses a bare target that is designed to produce fast neutrons with energies from 100 keV to almost 800 MeV. The measured neutron energy distribution from WNR is very similar to that of the cosmic-ray-induced neutrons in the atmosphere. However, the flux provided at the WNR facility is typically 5×107 times more intense than the flux of the cosmic-ray-induced neutrons. This intense neutron flux allows testing at greatly accelerated rates. An irradiation test of less than an hour is equivalent to many years of neutron exposure due to cosmic-ray neutrons.The low-energy neutron source is located at the Lujan Neutron Scattering Center. It is based on a moderated source that provides useful neutrons from subthermal energies to ∼100 keV. The characteristics of these sources, and ongoing industry program are described in this paper.

Fifteen years of the Protein Crystallography Station: the coming of age of macromolecular neutron crystallography.

The Protein Crystallography Station (PCS), located at the Los Alamos Neutron Scattering Center (LANSCE), was the first macromolecular crystallography beamline to be built at a spallation neutron source. Following testing and commissioning, the PCS user program was funded by the Biology and Environmental Research program of the Department of Energy Office of Science (DOE-OBER) for 13 years (2002-2014). The PCS remained the only dedicated macromolecular neutron crystallography station in North America until the construction and commissioning of the MaNDi and IMAGINE instruments at Oak Ridge National Laboratory, which started in 2012. The instrument produced a number of research and technical outcomes that have contributed to the field, clearly demonstrating the power of neutron crystallo-graphy in helping scientists to understand enzyme reaction mechanisms, hydrogen bonding and visualization of H-atom positions, which are critical to nearly all chemical reactions. During this period, neutron crystallography became a technique that increasingly gained traction, and became more integrated into macromolecular crystallography through software developments led by investigators at the PCS. This review highlights the contributions of the PCS to macromolecular neutron crystallography, and gives an overview of the history of neutron crystallography and the development of macromolecular neutron crystallography from the 1960s to the 1990s and onwards through the 2000s.