Radar emitter recognition plays an important role in military automated command and control system. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, a wavelet packet transform is used for feature extraction of unintentional modulation on pulse (UMOP). Then specific radar emitter recognition is achieved by a probabilistic SVM (PSVM). Experiments on simulation and actual radar data verify the correctness and validity of this method.

Few-shot learning aims to recognize new objects using a few labeled samples. Metric-based methods can effectively achieve this goal by building an appropriate feature space, for which a convolutional neural network (CNN) often acts as the backbone network. Unfortunately, CNN is adept at processing local spatial or channel neighborhood information, resulting in a lack of images’ global representation. In this paper, a global information embedding network (GIEN) is built for few-shot learning tasks. We have made two improvements in GIEN: one is the global information embedding (GIE) block, which captures the long-range dependencies of the images by a learnable frequency filter; the other is the two-branch training strategy, which combines cross-entropy loss and supervised contrastive loss to further promote discriminative features. Equipped with GIE, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on few-shot classification benchmark datasets such as mini ImageNet and tiered ImageNet.

Subspace clustering refers to the task of clustering a collection of points drawn from a high-dimensional space into a union of multiple subspaces that best fits them. State-of-the-art approaches have been proposed for tackling this clustering problem by using the low-rank or sparse optimization techniques. However, most of the traditional subspace clustering methods are developed for single-view data and are not directly applicable to the multi-view scenario. In this paper, we present a Manifold Regularized Multi-view Subspace Clustering (MRMSC) method to better incorporate the correlated and complementary information from different views. MRMSC yields a unified affinity representation by joint optimization across different views. To respect the data manifold locally, the graph Laplacian is constructed to maintain the intrinsic geometrical structure of each view. In the multi-view integration, a sparsity constraint is imposed to the unified affinity representation in order to better reflect the data relationship from multiple views or features. In experiments, we compared the performance of clustering using MRMSC with the single-view and concatenate-multi-view methods on different datasets. The results showed that better clustering performance can be achieved by fusing the multiple features with a unified affinity representation by MRMSC.

Radar emitter recognition is an important and challenging subject in radar signal analysis and processing. In this work, an ambiguity function (AF) representative-slice based feature extraction and optimization algorithm is presented for unintentional modulation recognition of moving radar emitters. It considers near-zero slices of AF as representative feature set of radar emitters, which not only coincides with the characteristics of real radar signals, but also mitigates the computation problem and avoids undesired cross terms in existing AF based method. Direct Discriminant Ratio (DDR) criterion is further utilized to preserve the most discriminant features and boost recognition accuracy, by ranking the kernel points along the representative-slice. Experimental results validate the practical usefulness and high stability of the proposed approach on real data of moving radar emitters, as well as synthetic radar data from U.S. Naval Research Laboratory.

Understanding the role of genetics in diseases is a challenging process that has multiple applications within functional genomics and precision medicine. In this paper, we present a general clustering method to identify disease genes under a multi-view setting. First, by incorporating the graph Laplacian of spectral clustering (SC) into the discriminative K-means, we obtain a single-view subspace representation, which is endowed with both discriminant power and geometrical structure information of that data layer. Then, integrating these individual subspaces together on the Grassmann manifold, we can further find a unified low-dimensional representation under the multi-view SC framework. The proposed two-stage method generalizes the single-view discriminative K-means and the multi-view Grassmann clustering, and can directly handle the case where both attribute-based data and interaction-based networks are available, which is extremely useful in biological research. As a case study of disease gene identification, we apply this method to a benchmark dataset that contains nine gene-by-term text profiles. Experimental results show that our method provides competitive results compared to the state-of-art clustering methods, including a similar one that fuses multiple kernels and Laplacians.

Due to the large number of targets and the close detection distance in indoor environments, a significant amount of ghost target points often appears in the imaging results of mmWave radar point clouds. These ghost points can distort the point cloud images and greatly reduce their readability. Nowadays, ghost elimination methods may affect the imaging accuracy of other targets, and the algorithm is complex, which is not suitable for application in narrow indoor environment. To mitigate interference caused by ghost image target points on already pointed cloud images, this paper proposes a ghost image removal method called Double-Parameter Elimination of Ghost Images (DPEGI) based on point cloud clustering. This method determines the occlusion relationship between each point cloud cluster by setting two key parameters. According to the relative scattering coefficient of the point cloud cluster, the ghost can be effectively removed while retaining the non-line-of-sight (NLOS) targets. Extensive verification using indoor measured data demonstrates that the proposed ghost elimination algorithm is capable of handling common indoor scenes while preserving over 90% of details with high accuracy, robustness, efficiency, and real-time processing capability.