Zongmin Zhao

In frequency-modulated continuous-wave (FMCW) radar systems, the detection of Doppler-extended targets (DETs) is a critical challenge. The micro-Doppler effects induced by the motion of extended targets such as pedestrians cause the echo energy to spread along the Doppler dimension. As a result, a single range-Doppler cell is unlikely to form a pronounced amplitude peak above the background noise level. Consequently, existing constant false alarm rate (CFAR) methods that rely on single-cell amplitude decisions tend to suffer from performance degradation in DET scenarios and exhibit limited adaptability under varying clutter conditions. To solve these issues, we propose a nonlinear transform–based variability index CFAR detector for DET (DET-NTVI-CFAR), with the aim of improving detection probability and maintaining stable false alarm control in complex clutter backgrounds. This work constructs a detection statistic by applying a nonlinear transform to the accumulated power cells and derives the threshold from the corresponding probability distribution model. A variability index CFAR (VI-CFAR) decision strategy is introduced to select the appropriate detection branch under different operating conditions. In the threshold design stage, the false alarm probability expressions of three sub-detection methods are derived to guide the selection of threshold parameters. Simulation results demonstrate that the proposed method achieves stable false alarm control and improves detection probability in various environments. Field test results also confirm the applicability of the DET-NTVI-CFAR detector.

Time difference of arrival (TDOA) localization enables high-accuracy positioning by analyzing arrival-time differences of target signals at distributed radar nodes, whose performance strongly depends on radar node topology. However, existing studies tend to focus more on improving localization accuracy, while overlooking the impact of radar geometric layout and surveillance coverage on localization performance. To this end, this paper proposes a topology optimization method for a distributed radar system based on an improved non-dominated sorting multi-objective particle swarm optimization (NS-MOPSO) algorithm. A geometric localization model is developed for a distributed TDOA radar system. Based on this model, three optimization objectives are formulated, including minimizing geometric dilution of precision (GDOP), maximizing target coverage, and improving the geometric balance of node placement. These three objective functions are incorporated into the NS-MOPSO framework to achieve a more reasonable radar geometric distribution. To enhance the optimization performance, a series of strategies are adopted, such as non-dominated sorting for Pareto-based solution selection, an improved crowding-distance scheme to encourage balanced multi-objective optimization, and Gaussian mutation to increase solution diversity and reduce the risk of premature convergence. To validate the proposed method, both simulation studies and real-world experiments were conducted under different node deployment scenarios. The results show that the optimized topology achieves a 6.4% reduction in RMSPE and a 4.3% increase in the proportion of high-quality localization regions compared with the best-performing comparative method, while also demonstrating faster convergence and improved stability. These findings confirm the effectiveness and robustness of the proposed approach in enhancing localization accuracy, expanding effective coverage, and improving overall system performance.

In symmetric α-stable (SαS)-distributed impulsive noise, classical direction of arrival (DOA) estimators degrade markedly due to heavy-tailed statistics. To address this, we propose an adaptive, gridless DOA method for coprime arrays. First, we design a condition-selection phased fractional lower-order moment scheme that determines the fractional order by minimizing the condition number, thereby accommodating varying noise intensities without prior knowledge of the characteristic index α. Then, leveraging the virtual uniform linear array formed by the coprime difference coarray, we establish a covariance-matching gridless sparse reconstruction framework to estimate DOAs, eliminating angular grid mismatch and internalizing regularization selection within a constrained optimization, thus avoiding manual tuning. Numerical simulations indicate that, across a broad range of generalized signal-to-noise ratios, snapshot counts, and noise intensities, the proposed method delivers overall gains in estimation accuracy and resolution over existing approaches. Real-world validation using a millimeter-wave radar sensor further verifies its detection performance and practical utility.

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.

The sparsity and uncertainty of millimeter-wave (MMW) radar point clouds make it difficult to extract features for matching with images, leading to a major challenge for targetless radar-camera extrinsic calibration. To address this problem, we propose a differentiable calibration method based on detection attributes, enabling targetless extrinsic calibration between MMW radar and camera. Our approach utilizes a three-branch neural network to extract cross-modality features between point clouds and images. By leveraging the dynamic properties and radar cross-section values of detected objects, the sparse MMW radar point clouds are augmented, and by calculating the relevance between paired features, the impact of uncertainty in MMW radar detection is resolved. Subsequently, a differentiable probabilistic Perspective-n-Point solver is employed to achieve end-to-end extrinsic parameter estimation without relying on initial extrinsic parameters or specific calibration targets. On the pose dataset constructed from the nuScenes dataset, the proposed method achieved a registration accuracy of 90.10%. Additionally, real-world experiments validate its precision and robustness in radar-camera calibration, demonstrating its effectiveness in targetless scenarios.

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.

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks. Firstly, an MRW image encoder is used to extract salient feature representations from all time-Doppler images through contrastive learning. This can extract the representative vector of each image and also obtain the pre-training parameters of the MRW image encoder. During the pre-training process, large-scale residual networks, medium-scale residual networks, and small-scale residual networks are used to extract global information, texture information, and semantic information, respectively. Moreover, the time–channel weighting mechanism can allocate weights to important time and channel dimensions to achieve more effective extraction of feature information. The model parameters obtained from pre-training are frozen, and the classifier is added to the backend. Finally, the classifier is fine-tuned using a small amount of labeled data. In addition, we constructed a new dataset with eight dangerous activities. The proposed MRW-CN model was trained on this dataset and achieved a classification accuracy of 96.9%. We demonstrated that our method achieves state-of-the-art performance. The ablation analysis also demonstrated the role of multi-scale convolutional kernels and time–channel weighting mechanisms in classification.