Rotating phased array radars utilize advanced technology for meteorological observations; however, these radars may experience misalignment in azimuth pointing. This study proposes a calibration approach for azimuth pointing in rotating phased array radars based on ground clutter (GC) correlation. First, the proposed method adaptively determines the number of clutter bins for each direction in the plan position indicator. Second, the azimuth calibration numbers are derived by optimizing the correlation between the GC echoes of two successive scans. Third, in light of the radar’s physical rotation constraints, the proposed method includes a process for handling situations when GC is not available. The effectiveness of the proposed method was experimentally assessed using data from thousands of rays collected under both rainy and clear-air conditions. The results obtained using the proposed method demonstrate an improvement compared with radar readings using standard angle encoders. This azimuth pointing calibration method is easy to carry out and achieves more accurate azimuth angles than current methods; it has the potential for use in operational radars.

Spectral polarization filtering in the range-Doppler domain plays an important role in weather radar clutter mitigation. However, when ground clutter and precipitation overlap, these methods tend to filter out clutter-contaminated precipitation, leading to estimation errors. To address this problem, this paper proposes a ground clutter and noise mitigation method based on range-Doppler spectral processing for polarimetric Doppler weather radars. The proposed method can filter out clutter and noise and retain precipitation overlapped with clutter by analyzing the property differences between precipitation and clutter and noise in the range-Doppler spectrogram. Specifically, due to the spatial continuity of precipitation in the range-Doppler domain, the spectral moments (e.g., velocity and spectral width) are also continuous. In addition, polynomial fitting is used to compensate for the missing spectral moments, and the missing precipitation is recovered by Gaussian fitting using the computed spectral moments. The results demonstrate performance improvements after applying the proposed method to radar data collected by Chinese operational weather radars. The proposed method is compared with several other algorithms, and the comparison results show that the proposed method performs best in clutter and noise suppression and precipitation retention performance.

Polarimetric radar systems play a crucial role in enhancing microwave remote sensing and target identification by providing a refined understanding of electromagnetic scattering mechanisms. This study introduces the concept of polarization scattering regions as a novel tool for describing the polarization characteristics across three spectral regions: the polarization Rayleigh region, the polarization resonance region, and the polarization optical region. By using ellipsoidal models, we simulate and analyze scattering across varying electrical sizes, demonstrating how these sizes influence polarization characteristics. The research leverages Cameron decomposition to reveal the distinctive scattering behaviors within each region, illustrating that at higher-frequency bands, scattering approximates spherical symmetry, with minimal impact from the target shape. This classification provides a comprehensive view of polarization-based radar cross-section regions, expanding upon traditional single-polarization radar cross-section regions. The results show that polarization scattering regions are practical tools for interpreting polarimetric radar data across diverse frequency bands. The applications of this research in radar target recognition, weather radar calibration, and radar polarimetry are discussed, highlighting the importance of frequency selection for accurately capturing polarization scattering features. These findings have significant implications for advancing weather radar technology and target recognition techniques, particularly as radar systems move towards higher frequency bands.

Accurate measurement of the complex permittivity of plant is essential for remote-sensing applications. The complex dielectric properties of moso bamboo ( Phyllostachys heterocycla ) were measured from 0.5 to 18 GHz based on the coaxial transmission/reflection method. A high-precision numerically controlled machine was used to process the bamboo samples. Based on the measured data, the complex permittivity can be better modeled as a second-order polynomial about the moisture content than the traditional exponential function. The real parts of the relative dielectric permittivity decrease with the increase in the frequency, while the imaginary parts decline first and then rise. The dielectric permittivity of the longitudinal direction is larger than that of the cross-sectional direction due to the anisotropic fibrocyte structure of moso bamboo.

Though deep learning has brought transformative advances to the field of modulation recognition, conventional approaches typically rely on a large amount of labeled data, which is often difficult to obtain in real-world communication scenarios. Few-shot modulation recognition (FSMR), which aims to identify modulation formats with extremely limited training samples, serves as a key enabler for next-generation cognitive radio, intelligent spectrum management, and non-cooperative communications. However, existing neural network models are not inherently designed for few-shot learning (FSL) and cannot be directly applied to FSMR tasks. To address this gap, this paper proposes a spatio-temporal prototypical network (STPN) trained within a meta-learning framework. Through a lightweight multi-module design that sequentially captures spatial patterns and temporal dependencies, STPN effectively integrates hybrid feature extraction with prototype-based classification. In contrast to existing approaches, STPN features a streamlined architecture free from intricate operations that could compromise generalization. This advantage is especially crucial when the model is trained on numerous meta-tasks with only a few samples. Comprehensive experiments on public benchmarks show that STPN achieves superior classification accuracy over several baseline models, while also offering advantages in parameter efficiency and computational cost. Further analysis investigates the key parameters influencing model performance, and ablation studies confirm the individual contribution of each module. This work not only deepens the theoretical understanding of prototype-based FSL techniques but also establishes a practical framework applicable to other signal processing tasks that demand robust performance under limited labeled data.