A reliable method based on facet-based asymptotical model, geometrical optics and physical optics (GO-PO) is developed to calculate composite scattering from 3-D complex ship targets over a rough sea surface. The backward ray-tracing is described to identify the visibilities of patches to the incident wave and the reflected wave of other patches on the target and facets on the sea surface. Both the scattering from target, including higher orders of scattering and the interactions between the target and rough sea surface, can be treated handily by the proposed GO-PO method. The accuracy of the target scattering and composite scattering is demonstrated by comparing with Multi-level fast multipole method (MLFMM), method of equivalent currents, and four-path model. Moreover, numerical examples of radar cross section estimation, electric current distribution, and synthetic aperture radar imagery simulation from a real ship target over a randomly rough sea surface are presented and discussed.

A complete facet model for the backscattering from rough sea surface with breaking waves is proposed in this paper. In consideration of the spatial distribution of breaking waves on sea surface, the model is able to give a good interpretation to the “super events” under high sea states, such as sea spikes and high polarization ratios. Based on the proposed model, normalized radar cross section plots of sea surface under backscattering configuration are calculated and compared with measured data. The comparisons show that the proposed model is tractable to estimate the scattering from electrically large ocean surface under high sea states with accuracy and efficiency. In addition, the non-Gaussian statistics and spatial correlation properties of sea clutter are analyzed under different range resolutions and incident angles. The results show that high kurtosis value, which means a sea spike phenomenon, mostly happens in lower grazing angle and higher range resolution cases. The comparisons of simulated SAR images of sea surface with and without breaking waves also reveal that sea spikes and high polarization ratios are caused by breaking waves. All the simulation results prove that our model not only is able to explain the physical mechanism of the scattering but also can be applied to the analyses of statistical properties of sea clutter under high sea states.

A modified range-Doppler algorithm (M-RDA) for processing high squint synthetic aperture radar (SAR) echoes is proposed in this letter. Unlike the original RDA, the M-RDA directly corrects the range cell migration in the range frequency domain without interpolation, which greatly enhances its computational efficiency. When analyzing a high squint SAR echo signal, the approximation of the distance between the SAR antenna and the scene of interest leads to error in azimuthal compression. To address this problem, a fourth-order phase model is considered in the proposed algorithm to achieve accurate azimuthal compression. Finally, geometry correction is performed to obtain the final focused SAR images. The simulation results show that the proposed algorithm can process high squint SAR echoes effectively.

An antenna and an array with photonic bandgap (PBG) structures, which are cylindrical conformal microstrip antennas, both operating in X-band (10.2 GHz) are proposed. As shown in the simulation, PBG structures could suppress the surface wave propagating on substrate and balance the influence of cylindrical curvature at resonance frequency on antennas. The simulation results indicate that they have a higher gain and better directivity over the conventional antenna without PBG structure. Both of the antenna and the array are designed and manufactured, and the measurement results agree well with the simulations.

Two‐dimensional inverse synthetic aperture radar (2D ISAR) system can produce 2D electromagnetic images of a target, while failed to provide the 3D information. In this study, a novel method for 3D super resolution ISAR imaging is proposed via employing 2D U‐estimation of signal parameters via rotational invariance technique (U‐ESPRIT) and slice principle. First, the 3D scattered field data tensor is converted into a set of 2D matrix by stacking slices of the 3D data along one specific dimension. Then, 2D U‐ESPRIT is applied to extract isolated scatterers from each slice. Combined with least‐square method, complex scattering amplitudes are also estimated. Finally, 3D reconstructed scattering centres model with specific locations and complex scattering amplitudes is obtained, which can characterise the target to a great extent. The reverse process, reconstruction of the 3D ISAR image, can be readily done in real time by means of the extracted scattering centres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

Traditional wake detection methods have been successfully applied to the detection of a simple linear ship wake. However, they cannot effectively detect nonlinear wake and weak wake under high sea state conditions, whereas the deep-learning-based detection method could play to its strengths in this respect. Due to the lack of sufficient measured SAR images of ship wake to meet the training requirement for deep learning method, this paper explores the method to detect the nonlinear ship wake by combining electromagnetic scattering model with deep learning technique. The composite scene model of the sea surface and its wake is established first, then the facet scattering distribution of the ship wake and the sea background is analyzed with the help of the electromagnetic scattering model, and the simulation of the wake SAR images under the sea background is finally accomplished based on the modulation model. Combined with the simulation results and measured wake SAR images, the sample database is constructed. The You Only Look Once Version five algorithm (YOLOv5) based on deep learning techniques is applied to detect the wake target in complex conditions such as different sea states, multiple targets, curvilinear wakes, and weak wakes. The result show that the YOLOv5 leads to an obvious higher detection efficiency with satisfactory accuracy. Moreover, the comparison between YOLOv5 and the traditional Radon transform method for detecting nonlinear wakes in a strong noise background shows that the proposed method is better than the traditional object detection model. Thus, the proposed scheme would be a practical tool to deal with the detection of nonlinear ship wake and weak wake in complex scenarios, which will be helpful to the further remote sensing investigation of the ship.