Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers.

This paper proposes a robust method for feature-based matching with potential for application to synthetic aperture radar (SAR) automatic target recognition (ATR). The scarcity of measured SAR data available for training classification algorithms leads to the replacement of such data with synthetic data. As attributed scattering centers (ASCs) extracted from the SAR image reflect the electromagnetic phenomenon of the SAR target, this is effective for classifying targets when purely synthetic SAR images are used as the template. In the classification stage, following preparation of the extracted template ASC dataset, some of the template ASCs were subsampled by the amplitude and the neighbor matching algorithm to focus on the related points of the test ASCs. Then, the subset of ASCs were reconstructed to the world view vector feature set, considering the point similarity and structure similarity simultaneously. Finally, the matching scores between the two sets were calculated using weighted bipartite graph matching and then combined with several weights for overall similarity. Experiments on synthetic and measured paired labeled experiment datasets, which are publicly available, were conducted to verify the effectiveness and robustness of the proposed method. The proposed method can be used in practical SAR ATR systems trained using simulated images.

Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models.

This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than -5 dB.

Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter.

This paper deals with joint tracking and classification (JTC) of multiple targets based onscattering center model (SCM) and wideband radar observations. We first introduce an SCM-basedJTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP)with the information of the target aspect angle, and target classification is implemented through thedata correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-targetJTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTCmethod into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter withBayesian theory. To further tackle the complex integrals' calculation involved in targets state andclass estimation, we finally provide the sequential Monte Carlo (SMC) implementation of theproposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC methodis validated by simulation results under the application scenario of maritime ship surveillance.

Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region.

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.

Electron-Hole Symmetry Breaking in Charge Transport in Nitrogen-Doped Graphene.

Graphitic nitrogen-doped graphene is an excellent platform to study scattering processes of massless Dirac Fermions by charged impurities, in which high mobility can be preserved due to the absence of lattice defects through direct substitution of carbon atoms in the graphene lattice by nitrogen atoms. In this work, we report on electrical and magnetotransport measurements of high-quality graphitic nitrogen-doped graphene. We show that the substitutional nitrogen dopants in graphene introduce atomically sharp scatters for electrons but long-range Coulomb scatters for holes and, thus, graphitic nitrogen-doped graphene exhibits clear electron-hole asymmetry in transport properties. Dominant scattering processes of charge carriers in graphitic nitrogen-doped graphene are analyzed. It is shown that the electron-hole asymmetry originates from a distinct difference in intervalley scattering of electrons and holes. We have also carried out the magnetotransport measurements of graphitic nitrogen-doped graphene at different temperatures and the temperature dependences of intervalley scattering, intravalley scattering, and phase coherent scattering rates are extracted and discussed. Our results provide an evidence for the electron-hole asymmetry in the intervalley scattering induced by substitutional nitrogen dopants in graphene and shine a light on versatile and potential applications of graphitic nitrogen-doped graphene in electronic and valleytronic devices.

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.