For the issue of stealth and electromagnetic protection of weapons and equipment in strong electromagnetic pulse (EMP) environment, a transparent double-sided ultra-wideband metasurface is proposed to realize wave absorption/ absorption-scattering integrated function in two directions. The interactions between backscattered waves produced by the meta-atoms are manipulated and optimized simultaneously by hybrid far-field scattering pattern analysis and genetic algorithm to achieve ultra-wideband destructive interference. Both simulation and experimental results demonstrate that the polarization-independent -10 dB radar cross section (RCS) reduction performance can be achieved for more than 128% of the fractional bandwidth (4-18.25 GHz) at forward incidence, and the absorption more than 90% in the range of 2.75-12.75 GHz (fractional bandwidth more than 129%) at backward incidence. The scattering and absorbing characteristics of the metasurface are still well maintained in the range of 0°-50° oblique incidence. The method of this paper has an important prospect in related applications of strong EMP stealth and protection technology under the integration of information equipment in the future.

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.

The vortex electromagnetic (EM) wave, which carries orbital angular momentum (OAM), is exploited to multitarget detection applications. The detection model in the squint mode is derived first. The elevation and azimuth angles of the targets are estimated by the fast Fourier transform method and validated by the simulation. Subsequently, proof-of-concept experiments are performed to validate the principle of the proposed OAM-based target detection technique. The signal processing method for the real-world data obtained from the experiments is proposed to identify the positions of the targets. Experimental results demonstrate the effectiveness of the proposed detection method and indicate that the vortex EM wave can readily be applied to detect multiple targets simultaneously. The work and results show the new applications of the vortex EM wave as well as the development of the novel radar detection technology.

Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

Aiming at problem of management of minefield detection in mine operations. In this paper, the minefield grid model and mine-hunting sonar detection model are established. On this basis, the ordinal edge table algorithm is proposed to calculate minefield detection, which is simple and easy to implement. The example shows that the method can accurately calculate and conveniently manage minefield detection that can assist commander to view and judge, which improve the efficiency of mine operations.