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.

In this article, a twofold physical model (PM) embedded 3-D U-Net with polarization fusion (PF), referred to as PUP-Net, is proposed for solving 3-D inverse scattering problems (ISPs) with a sparse planar array. To mitigate the high nonlinearity of ISPs, the twofold PM, consisting of the coherence factor back-projection algorithm (CF-BPA) and cross-correlated subspace-based optimization method (CC-SOM), first recovers the preliminary quantitative co-polarization images with high-fidelity geometry. The preliminary results are then input into the 3-D U-Net-based convolutional neural network (CNN), which outputs the fine contrast images of co-polarization (HH and VV). Finally, the upsampling PF achieves the co-polarization image fusion and generates higher accuracy and resolution results. PUP-Net follows the divide-and-conquer method: refine quantitative inversion based on good qualitative inversion. Synthetic and experimental inversion results demonstrate that PUP-Net achieves superior reconstruction accuracy and generalization ability compared with conventional iterative methods and current deep learning (DL) approaches in solving ISPs of half-space configurations.

The detection and identification process in radar is susceptible to disturbances from numerous non-cooperative jamming signals in addition to target signals. Accurately estimating the parameters of these non-cooperative signals is crucial for extracting the target signal. Addressing the current lack of effective solutions for estimating parameters of different types of uncooperative combined signals, this paper proposes a method for parameter estimation of non-cooperative composite signals based on joint time-polarization domain separation. This method combines amplitude and phase information from both the time and polarization domains of non-cooperative signals and integrates the JADE separation algorithm to achieve the separation and estimation of specific non-cooperative signal combinations. Experiments compare the separation performance of this method with other signal separation algorithms for non-cooperative jamming signal combinations. The results indicate that, at jamming-to-noise ratio of −10 dB to 15 dB, this method achieves better separation accuracy and precision for specific non-cooperative jamming combinations, while also accurately estimating the parameters of the non-cooperative jamming signals.