Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

With the swift advancement of deep learning, significant strides have been made in Polarimetric Synthetic Aperture Radar (PolSAR) image classification, particularly with the advent of diffusion models that allow for explicit class probability modeling. However, existing diffusion models have yet to fully leverage the rich polarimetric characteristics of PolSAR images. To address this, we propose the Multi-level Conditional Diffusion (MCDiff) model for PolSAR image classification, incorporating three key strategies. Firstly, a Prior Learning (PL) module is constructed to capture scattering characteristics across all three polarization basis parameter spaces, providing conditional guidance for the diffusion model. Secondly, a Multi-scale and Multi-dimensional noise Prediction (MMP) module is designed to reduce the information loss when noisy labels and image features of different dimensions are fused to predict noise. Finally, a Multi-level high-order Statistical feature Learning (MSL) module is introduced to aid in the additive Gaussian noise prediction of noisy labels while mitigating the impact of PolSAR images' multiplicative speckle noise on the prediction. Experimental results on three benchmark datasets confirm MCDiff's ability to achieve high-performance explicit class probability modeling for PolSAR images among the compared methods

In the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. Given the challenges faced by deep learning methods in scenarios with limited labeled data and insufficient prior information, CFAR detection techniques remain vital in resource-constrained environments requiring rapid response. However, in the context of CFAR detection in PolSAR imagery, traditional intensity-based statistical modeling approaches, such as gamma distribution, generalized gamma distribution, and log-normal distribution, become inadequate when handling feature maps generated by non-positive definite transformation matrices. Additionally, some non-parametric methods suffer from a lack of robust statistical theoretical support, resulting in insufficient robustness. To effectively address this core issue, this study proposes a general framework for CFAR based on quadratic statistics, employing both the numerical solution of the Gil-Pelaez inversion formula and Monte Carlo reinforcement as strategies to determine detection thresholds. Experimental results from both simulated and real data demonstrate that this approach exhibits superior detection performance across diverse application scenarios, significantly improving the accuracy and robustness of target detection in PolSAR images. The key strength of this framework lies in providing a novel, unified solution for PolSAR image processing, effectively integrating the rigor of statistical analysis for practical applications.

The levitation and guidance system of the superconducting electrodynamic levitation(EDS) maglev vehicle includes superconducting magnets and 8-shaped levitation coils. Based on the dynamic theory, the mathematical model of the levitation system is established. Levitation characteristics of the superconducting EDS maglev vehicle are analyzed. The levitation force and guiding force of the vehicle will gradually increase to saturation as the speed increases, and the magnetic resistance will first increase and then decrease. The magnetic resistance is much smaller than the levitation force, and the buoyancy-to-resistance ratio is large. It proves that the vehicle has a good buoyancy-to- resistance ratio when running at high speed. And the levitation stiffness is conducive to the stable operation of the vehicle. It is also concluded that as the vehicle sinks, the levitation force will first increase and then decrease.

Syntheticaperture radar (SAR) and optical (OPT) image fusion aims to leverage their complementary information to obtain a more comprehensive representation of ground objects. However, significant discrepancies exist between the two modalities in terms of imaging mechanisms and feature distributions. Consequently, existing multi-modal image fusion methods struggle to achieve robust cross-modal feature alignment and deep semantic consistency between the fused results and the source modalities. To address the above challenges, this paper proposes a correlation and semantic prior-guided multi-scale cross-modal interaction network (CSP-MCIN) for effective SAR-OPT image fusion. Specifically, CSP-MCIN first employs two modality-specific encoders based on ResNet-18 to extract low-level details and high-level semantic features from SAR and OPT images, respectively. Subsequently, a multi-scale interactive decoder integrating cross-modal Transformers and gated fusion units is constructed to align and aggregate semantic and detail information from both encoders. Finally, to strengthen source-modality representations, a novel loss function combining a pixel-domain correlation loss and a CLIP-guided semantic consistency loss is designed and optimized under a PCGrad-based multi-objective optimization scheme. Experimental results on public SAR-OPT image datasets demonstrate that the proposed CSP-MCIN achieves superior fusion performance and computational efficiency compared with state-of-the-art approaches.