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Fast construction of the 3-D scattering centers of ship targets on the sea surface is important for many radar applications, including the fast signature prediction, feature extraction, and automatic recognition of targets. Combining the “four-path” model for target-surface coupling scattering with modified Fresnel reflection coefficient model in the stochastic sea surface and ray tube integration method, we propose a 3-D image formation method for ship-surface compound targets. Using the CLEAN technique on 3-D image, we develop a fast algorithm for establishing 3-D scattering center model for ship targets on the sea surface. Because this algorithm realizes 3D imaging of targets at a single frequency and single aspect angle, and adopts simplified surface model to avoid the need to construct a large number of surface elements, the computational efficiency of the proposed alogrithm is greatly increased to meet the needs of practical engineering applications. Simulation experiments of a typical ship target show that the proposed algorithm can increase the speed by four orders of magnitude under typical conditions, as compared with the traditional FFT-based 3D imaging method. We validate the accuracy of this algorithm by comparing reconstructed 1-D range profiles and ISAR images obtain by the scattering center model with the ones that are directly simulated.

Classification of drones is important due to their increasing popularity and potential threats. The micro-Doppler signatures that depend on the rotation of rotor blades facilitate the classification of drones. To enhance the robustness of micro-Doppler based classification of drones, dual radar sensing classification scheme is proposed in this paper. First, time-frequency spectrograms are obtained by performing a short-time Fourier transform on the radar data collected by two radar sensors that have similar angular diversity. Then, principal components analysis is utilized to extract the features from the time-frequency spectrograms and the features obtained by the two radar sensors are fused together. Finally, the classification results are obtained by using the support vector machine. The experimental results show that the classification accuracy obtained by the fusion of dual radar sensors is 5% higher than that obtained by only using a single radar sensor.

The measurement system has been built with Stepped Frequency Continuous Wave (SFCW) signal for Radar Cross Section (RCS) measurements in free-space at 220～330 GHz. The system consists of vector network analyzer, Millimeter-Wave Converters and target support structure. The measurement techniques which are used in scattering measurements are verified by RCS measurement data. The system measurement accuracy is control within about ±3 dB at –23.6 dBsm.

The phonon dispersion spectrum, eigenvector, and lattice vibration frequency of cadmium telluride with a zinc blende structure have been investigated using the density functional theory, and the permittivity of cadmium telluride crystal is numerically calculated. The permittivity of the crystal is measured using the terahertz time-domain spectroscopy system. The experimental results are consistent with the theoretical calculations on the modified local density approximation, the general gradient approximation, and the modified general gradient approximation. Finally, the differences among the three approximate exchange correlation potentials indicate that in the terahertz region, the permittivity of cadmium telluride is dominantly contributed by the coupling between electron and phonon; however, the phonon frequencies of transverse wave and longitudinal wave were sensitive to electron density distribution.

Automatic Target Recognition (ATR) is one of the most difficult problems in Synthetic Aperture Radar (SAR) data interpretation. In recent years, the model-based SAR target recognition method has attracted much attention because of its good performance in the extended operation condition. Based on the research of a few domestic research institutes, this paper briefly introduces the preliminary research results and gives some thoughts about SAR ATR problem. First of all, the development of parametric scattering model are discussed from three aspects. Next, two ways to model the parametric electromagnetic scattering for complex target are put forward. Finally, we propose a new framework for a Three-Dimensional (3D) parametric scattering model based SAR ATR. In the end, the future research direction of model-based SAR target recognition is prospected.

Knowledge of target polarization characteristics is valuable for radar target detection, classification, and identification. We conducted experimental research on an Unmanned Aerial Vehicle (UAV) with complex materials and structures to determine the differences in polarimetric scattering between the UAV and its perfect electric conductor model. To illustrate the coherence of the entire UAV and its components using polarimetric scattering, we measured and analyzed each part. The results reveal that the airframe and aerofoils directly influence the depolarization, and that the polarimetric scattering characteristics of the airframe represent the primary source for the whole UAV.

Traditional hidden surface removal algorithm based on hardware Z-Buffer technique cannot give attention to precision or efficiency at the same time when dealing with the non-uniform triangulated SAR (Synthetic Aperture Radar) scene model. A novel high-precision hidden surface removal approach using fast ray-tube splitting algorithm is proposed, where the SAR scene hidden surface removal problem is decomposed into two simple procedures, i.e. a Delaunay triangulator is used to generate the initial ray tubes from the projected point clouds of all incident visible vertices, then an adaptive ray-tube splitting method is adopted to carry out the complex scene shading situations and resultant visible model reconstruction. Simulation results of typical aircraft and T-72 tank show that, the new approach is feasible and effective.