This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.

Traditional direction of arrival (DOA) estimation algorithms fail to account for the impact of nonuniform noise in practical applications, leading to model mismatch and inaccurate DOA estimation. To overcome this issue, we propose a robust gridless sparse iterative estimation-based DOA algorithm using coprime arrays. First, the received signal model is constructed, and adaptive reconstruction of the covariance matrix is achieved by setting the diagonal elements to their minimum values based on the characteristics of nonuniform noise. Subsequently, leveraging the sparsity of DOAs in the spatial domain, interpolation and overcomplete representation of the second-order received signals in the virtual domain are performed. A covariance fitting optimization problem is then formulated using a gridless sparse iterative estimation method, achieving high-precision DOA estimation and enhanced degrees of freedom. Furthermore, we employ an alternating projection strategy in the optimization process, effectively reducing the computational cost associated with complex iterative procedures. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, and its performance is further demonstrated using real-world measurements from millimeter-wave radar sensors.

The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

This paper addresses the challenge of small-object detection in traffic surveillance by proposing a hybrid network architecture that combines attention mechanisms with convolutional layers. The network introduces an innovative attention mechanism into the YOLOv8 backbone, which effectively enhances the detection accuracy and robustness of small objects through fine-grained and coarse-grained attention routing on feature maps. During the feature fusion stage, we employ adaptive dilated convolution, which dynamically adjusts the dilation rate spatially based on frequency components. This adaptive convolution kernel helps preserve the details of small objects while strengthening their feature representation. It also expands the receptive field, which is beneficial for capturing contextual information and the overall features of small objects. Our method demonstrates an improvement in Average Precision (AP) by 1% on the UA-DETRAC-test dataset and 3% on the VisDrone-test dataset when compared to state-of-the-art methods. The experiments indicate that the new architecture achieves significant performance improvements across various evaluation metrics. To fully leverage the potential of our approach, we conducted extended research on radar–camera systems.