Aiming at the existing deep learning radar signal modulation recognition methods are mostly based on time-frequency image (TFI) and consequently result in networks with a large number of parameters due to the significant amount of redundant information contained in TFI, this paper proposes a radar signal intra-pulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the lightweight characteristics of PointNet++ make the operation of the method more efficient.

Crowdsourcing provides a cost-effective solution to the problem of obtaining large annotated datasets. In real-world crowdsourcing scenarios, most workers often annotate a few instances only, which results in a significantly sparse crowdsourcing label matrix and subsequently harms the performance of label integration algorithms. Probabilistic matrix factorization (PMF) has been proven to be an effective method for crowdsourcing label completion. However, its low-quality input and output labels limit its performance. To improve its performance, this paper proposes a PMF-based three-stage label completion (PMF-TLC) method. In the first stage, we design a label confidence-based strategy to estimate the quality of each raw label of each worker. Then we flip those low-quality labels in the original crowdsourcing label matrix. In the second stage, we conduct PMF on the flipped label matrix and obtain the completed label matrix with soft labels. In the third stage, we design a between-class margin-based filter to delete those low-quality soft labels in the completed label matrix. Then we convert the remaining high-quality soft labels to hard (logic) labels and obtain the final processed label matrix. Extensive experimental results on real-world and simulated crowdsourced datasets show that PMF-TLC can significantly improve label integration algorithms' performance.

In this paper, a clustering algorithm based on the MaskRCNN instance segmentation network is proposed to address the radar pulse stream clustering problem. Compared with traditional algorithms, the suggested algorithm successfully avoids the problem of preset parameters and algorithm structure constraints by using instance segmentation networks as clustering algorithms, and provides an end-to-end solution for the radar signal sorting issue. A PDW lattice mapping method is proposed to adapt to the input form of the instance segmentation network. The instance segmentation network based on ResNet, FPN, RPN, and RolAlign structure detects and masks the individual from the same or different radar signals. The simulation results show that the suggested algorithm performs the sorting of radar pulse stream under the condition of serious overlapping of radar parameters.