This letter presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based radar. With the known radar scanning velocity, the difference time of the scan time can be converted into a subtended angle, which has linear relationship with the angle of arrival (AOA). The best linear unbiased estimator is applied for optimal and reliable AOA estimation. The weighted least square method is used for localization of the scan-based radar system. The presented solution is computationally efficient and statistically consistent in that the measured mean squared errors achieve the Cramer Rao lower bounds (CRLB).

Point cloud analyzing and processing have attracted extensive attention due to their broad application in numerous sectors. Although many previous deep learning-based frameworks have had significant improvement, they often struggle with processing efficiency and neglect the spatial relationships between points. In this paper, we introduce CSP-Former, a novel framework for point cloud classification and segmentation. Inspired by the impressive strides of self-attention obtained in NLP and CV tasks, we designed a transformer-based network as a backbone for feature extraction; additionally, a fast sampling layer based on compressed sensing theory is proposed to enhance the sampling efficiency, which speeds up the sampling process through only once matrix multiplication. Subsequently, a hierarchical spatial self-attention module is also proposed to better capture the spatial relationships between points, improving segmentation performance. Extensive experiments on the ModelNet40 and ShapeNet part datasets demonstrate that our proposed framework achieves superior performance in point cloud classification and segmentation tasks.

Precipitation nowcasting is a critical part of meteorological services and applications. Recently, mainstream research has been focused on adopting deep learning-based models to generate the predictions, yet existing deep learning models face challenges with blurry predictions that fail to capture high-frequency meteorological details, difficulty modeling both local correlations and long-range spatial dependencies, and a fundamental signal–noise confusion within the diffusion process that degrades structural fidelity. In this paper, we propose FADiff, a novel frequency-aware diffusion model based on a hybrid CNN–Transformer network for radar-based precipitation nowcasting. A hybrid CNN–Transformer backbone is first designed to integrate the CNNs with the Transformers, jointly enabling the local and global feature extraction capability of the meteorological dynamics. Subsequently, a novel Frequency-Aware Module (FAM) is proposed to mitigate signal–noise confusion. By transforming features into the frequency domain via the Discrete Cosine Transform (DCT), the FAM performs content-adaptive filtering with a learnable gating mechanism, which is designed to suppress noise-dominant frequency components while benefiting high-frequency signals corresponding to real meteorological structures. Finally, these components are embedded within a latent diffusion model to form an end-to-end nowcasting framework. Extensive experiments on the CIKM and SEVIR datasets demonstrate that the proposed FADiff outperforms state-of-the-art methods across a comprehensive suite of evaluation metrics. Significantly, under high-intensity precipitation thresholds, FADiff exhibits remarkable robustness and stability, presenting its superior capability in generating meteorologically critical structures with high fidelity.