LiDAR, as an excellent sensor, can provide positions, motion states, and other objective attribute information of objects in the 3D world. Inevitably, the inherent sparsity of point cloud and the problem of occlusion tend to cause incomplete semantic and geometry information of long-range small objects, posing challenges to 3D object detection. The multi-view models take advantage of the complementary information among bird’s eye view (BEV), range view (RV), and other views to alleviate the above issues. However, most of the existing methods coarsely learn the views’ features and neglect the learning of semantic information, which further leads to unsatisfactory detection performance. To this end, this paper proposes a Local-to-Global Semantic Learning Network (LGSLNet) for multi-view 3D object detection from point cloud. The proposed LGSLNet can effectively learn semantic information to explore the local semantics contained in various channels of RV features and to fuse them with BEV features. It has two branches with different backbones. In the BEV branch, the voxels quantized from the point cloud are extracted by sparse convolutional networks and compressed to BEV features. In the RV branch, a multi-scale backbone with semantic-aware convolution (SAC) is designed to learn the local semantic information of the RV. It allows for adaptation to the 3D location using the auxiliary network. In the fusion module, the bidirectional cross-view channel attention (Bi-CCA) is designed to compensate for the semantic information between multiple views and aggregate new RV and BEV features. Extensive experiments on the KITTI, ONCE, and nuScenes 3D object detection datasets demonstrate the superiority of our proposed method.

The iterated‐corrector probability hypothesis density (IC‐PHD) filter propagates the posterior intensity of each sensor at one time step to improve tracking accuracy. However, targets cannot be estimated by the IC‐PHD filter, if the detection probability of the last update sensor is low. To deal with this problem, this study presents a new multi‐sensor multi‐target tracking method. Analysing the iterative process of this filter, it can be observed that the measurements obtained by sensors can be divided into several measurement subsets. Then, the similarity among the measurements is described by two parts, the similarity by combining the posterior intensity updated by the measurement, and the credibility of the posterior intensity. The similarity can be used to determine whether the measurements are from one target and whether the measurement selection meets the real situation. Based on the similarity among measurements, a two‐way selection approach is presented to find out measurement subsets corresponding to the true targets. In this approach, measurement and measurement subset are mutually selected. Two measurements are also mutually selected. Thus only the measurement subsets corresponding to true targets have large weights. The simulation results show that the proposed method can reduce the miss‐detection and false alarm effectively.

Motion models are vital for solving multiple object tracking (MOT), which makes instance-level position predictions of targets to handle occlusions and noisy detections. Recent methods have proposed the use of Single Object Tracking (SOT) techniques to build motion models and unify the SOT tracker with the object detector into a single network for high-efficiency MOT. However, three feature incompatibility issues in the required features of this paradigm are ignored, leading to inferior performance. First, the object detector requires class-specific features to localize objects of pre-defined classes. Contrarily, target-specific features are required in SOT to track the target of interest with an unknown category. Second, MOT relies on intra-class differences to associate targets of the same identity (ID). On the other hand, the SOT trackers focus on inter-class differences to distinguish the tracking target from the background. Third, classification confidence is used to determine the existence of targets, which is obtained with category-related features and cannot accurately reveal the existence of targets in tracking scenes. To address these issues, we propose a novel Task-specific Feature Encoding Network (TFEN) to extract task-driven features for different sub-networks. Besides, we propose a novel Quadruplet State Sampling (QSS) strategy to form the training samples of the motion model and guide the SOT trackers to capture identity-discriminative features in position predictions. Finally, we propose an Existence Aware Tracking (EAT) algorithm by estimating the existence confidence of targets and re-considering low-scored predictions to recover missed targets. Experimental results indicate that the proposed Discriminative Motion Model-based tracker (DMMTracker) can effectively address these issues when employing SOT trackers as motion models, leading to highly competitive results on MOT benchmarks.

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB.

Accurate 3D object detection is becoming increasingly vital for the development of robust perception systems, particularly in applications such as autonomous driving vehicles and robotic systems. Many existing approaches rely on bird’s eye view (BEV) feature maps to facilitate multi-modal interaction, as BEV representations enable efficient operations. However, the inherent sparsity of LiDAR BEV features often leads to misalignment with the dense semantic information in camera images, resulting in suboptimal fusion quality and degraded detection performance, especially in complex and dynamic environments. To mitigate these issues, this paper proposes a novel multi-dimension semantic–spatial feature fusion (MDFusion) method that combines LiDAR and image features in 2D and 3D spaces. Specifically, image semantic features are extracted using the DeepLabV3 segmentation network, which captures rich contextual information and is aligned with LiDAR point cloud voxel features through a summation operation to achieve precise semantic fusion. Additionally, LiDAR BEV features are fused with downsampled image features in 2D space via concatenation and spatially adaptive dilated convolution. The mechanism dynamically adjusts to the spatial characteristics of the data, ensuring robust feature integration. Extensive experiments on the KITTI and ONCE datasets demonstrate that our method achieves competitive performance in complex scenes, significantly improving the multi-modal fusion quality and detection accuracy while maintaining computational efficiency.