Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “Science”, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.

Due to the high-precision angle measurement performance, the monopulse technique plays a key role in fields such as remote sensing and space surveillance. The accuracy of monopulse angle measurement depends on the received amplitude and phase information, which is sensitive to the polarization component. Previous research has demonstrated that the performance of monopulse radar equipped with a parabolic antenna suffers from the cross-polarization component. However, it is not clear whether phased arrays (PAs) with higher degrees of freedom will also be affected by the cross-polarization component, and the parameter tolerance for performance degradation remains uncertain. In this paper, we establish a mathematical model of monopulse angle measurement in PA radar, which provides a comprehensive consideration of the cross-polarization component. Then, the received amplitude and phase patterns of PA radar are analyzed, and the theoretical angle errors caused by the cross-polarization jamming are derived. The experiments are conducted based on the measured amplitude-phase patterns of both co-polarization and cross-polarization. Experimental results are consistent with the theoretical analysis: the angle errors caused by cross-polarization jamming can reach half of the beamwidth in both azimuth and elevation dimensions, provided that the power of the cross-polarization and co-polarization components at the receiver is equal.

Traditional synthetic aperture radar (SAR) deceptive jamming can effectively generate deceptive scenes or false targets in SAR images. However, these false targets or scenes can be easily distinguished or eliminated by the multichannel SAR system. To interfere with the multichannel SAR, we first analyzed the results of SAR deceptive jamming generated by one transponder and two transponders against three-channel SAR- ground moving target indication (GMTI). Then, we propose a new deceptive jamming method against three-channel SAR-GMTI by using three synergetic transponders. By modulating each transponder with a complex coefficient, three synergetic transponders can generate false moving targets with the controllable radial velocity and located azimuth position in three-channel SAR-GMTI. Besides, in this paper, we also introduce an algorithm to deploy three transponders reasonably by utilizing the minimum condition number. In the end, a general architecture of multiple transponders deceiving multichannel SAR is given. The proposed method can not only generate deceptive false targets against multichannel SAR-GMTI, but also guide the production of a deceptive digital elevation model (DEM) against multichannel interferometric SAR (InSAR). Simulations verify the effectiveness of the proposed method.

For a three-dimensional wide-angle synthetic aperture radar (SAR) with non-uniform sampling, it is necessary to divide its large aperture into several small sub-apertures before imaging due to the anisotropic characteristics of the target. The existing sub-aperture partitioning methods divide the aperture with equal intervals. However, for the non-uniformly sampled SAR, those equal-interval partitioning methods may have a bad effect on the resolution of the SAR imaging result. In view of this, a sub-aperture partitioning method for three-dimensional wide-angle SAR imaging with non-uniform sampling was proposed in this paper. First, we analyzed the relationship between the three-dimensional resolution and the sampling distribution in K-space based on the Cramer–Rao lower bound. Subsequently, according to the distribution of K-space sampling, the optimum size of each sub-aperture was found and the aperture was divided non-uniformly. Furthermore, the proposed method was validated by electromagnetic simulation data. The proposed sub-aperture partitioning method ensured that the resolution of each sub-aperture was high and consistent. By comparing with the equal-interval partitioning method, the experimental results showed that our proposed method had a higher resolution imaging result.

As an emerging technique, sparse imaging from three-dimensional (3-D) and non-uniform samples provides an attractive approach to obtain high resolution 3-D images along with great convenience in data acquisition, especially in the case of targets consisting of strong isolated scatterers. Although data interpolation in k-space and fast Fourier transform have been employed in the existing 3-D sparse imaging methods to reduce the computational complexity, the data-gridding errors induced by local interpolation may usually result in poor imaging performance. In this paper, we directly regard the imaging problem as a joint sparse reconstruction problem from non-uniform data without interpolation in 3-D space. Combining dictionary reduction and Gauss iterative method with the optimized signal processing scheme, a sparse imaging algorithm is proposed to address the difficulty of large-scale computation involved in direct 3-D sparse reconstruction. Benefited from the optimized signal processing scheme and the avoidance of data interpolation, the direct 3-D sparse imaging (DTDSI) method proposed in this paper is of low computation scale and high imaging performance. Experiments of electromagnetic simulation data demonstrate the DTDSI method outperforms baseline methods in terms of resolving ability, lower side-lobes and higher accuracy.