To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic aperture radar (SAR) images. By constructing a hierarchical feature decoupling mechanism, the method decomposes input images into low-resolution global structural features and high-resolution local detail features. A residual compression module is employed to preserve multi-scale information, laying a complementary feature foundation for subsequent fusion. To address cross-modal radiometric discrepancies, a pre-trained complementary feature extraction model (CFEM) is introduced. The brightness distribution differences between SAR and fusion results are quantified using the Gram matrix, and mean-variance alignment constraints are applied to eliminate radiometric discontinuities. In the feature fusion stage, a dual-attention collaborative mechanism is designed, integrating channel attention to dynamically adjust modal weights and spatial attention to focus on complementary regions. Additionally, a learnable radiometric enhancement factor is incorporated to enable efficient collaborative representation of SAR textures and optical semantics. To maintain spatial consistency, hierarchical deconvolution and skip connections are further used to reconstruct low-resolution features, gradually restoring them to the original resolution. Experimental results demonstrate that MRLF significantly outperforms mainstream methods such as DenseFuse and SwinFusion on the Dongying and Xi’an datasets. The fused images achieve an information entropy (EN) of 6.72 and a structural similarity of 1.25, while maintaining stable complementary feature retention under large-scale scenarios. By enhancing multi-scale complementary features and optimizing radiometric consistency, this method provides a highly robust multi-modal representation scheme for all-weather remote sensing monitoring and disaster emergency response.

Low Probability of Intercept(LPI) radar signals utilize complex modulation techniques to achieve low intercept characteristics, making their accurate identification in complex electromagnetic environments a challenging task. To address this issue, we propose LW-BERSNet, an automatic modulation recognition method that combines adaptive wavelet decomposition with deep residual shrinkage networks. The method initially transforms the radar signal into a time-frequency image through time-frequency analysis. Thereafter, the Learnable Wavelet Band Enhancement (LWBE) module is applied to enhance the signal’s time-frequency feature representation during the automatic radar signal modulation recognition stage. In the final stage, leveraging the extracted time–frequency representations, a Deep Residual Shrinkage Network integrated with a Cross Entropy–Dynamic Center Loss (CE–DC Loss) function is designed for model training. Experimental results in environments with various complex multipath fading effects and colored noise demonstrate that the proposed method is highly adaptable to complex conditions, achieving a recognition accuracy of over 96% even at a signal-to-noise ratio of -10 dB.

Non-contact heart rate estimation technology based on frequency-modulated continuous wave (FMCW) radar has garnered extensive attention in single-target scenarios, yet it remains underexplored in multi-target environments. Accurate discrimination of multiple targets and precise estimation of their heart rates constitute key challenges in the multi-target domain. To address these issues, we propose a novel scheme for multi-target heart rate estimation. First, a high-precision distance-bin selection (HDBS) method is proposed for target localization in the range domain. Next, multiple-input multiple-output (MIMO) array processing is combined with the Root-multiple signal classification (Root-MUSIC) algorithm for angular domain estimation, enabling accurate discrimination of multiple targets. Subsequently, we propose an efficient method for interference suppression and vital sign extraction that cascades variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) termed as VLW, which enables high-quality heartbeat signal extraction. Finally, to achieve high-precision and super-resolution heart rate estimation with low computational burden, an improved fast iterative interpolated beamforming (FIIB) algorithm is proposed. Specifically, by leveraging the conjugate symmetry of real-valued signals, the improved FIIB algorithm reduces the execution time by approximately 60% compared to the standard version. In addition, the proposed scheme provides sufficient signal-to-noise ratio (SNR) gain through low-complexity accumulation in both distance and angle estimation. Six experimental scenarios are designed, incorporating densely arranged targets and front-back occlusion, and extensive experiments are conducted. Results show this scheme effectively discriminates multiple targets in all tested scenarios with a mean absolute error (MAE) below 2.6 beats per minute (bpm), demonstrating its viability as a robust multi-target heart rate estimation scheme in various engineering fields.

This paper proposes a novel heart rate detection scheme to address key challenges in millimeter-wave radar-based vital sign monitoring, including weak signals, various types of interference, and the demand for high-precision and super-resolution frequency estimation under practical computational constraints. First, we propose a multi-dimensional coherent accumulation (MDCA) method to enhance the signal-to-noise ratio (SNR) by fully utilizing both spatial information from multiple receiving channels and temporal information from adjacent range bins. Additionally, we are the first to apply the fast iterative interpolated beamforming (FIIB) algorithm to radar-based heart rate detection, enabling super-resolution frequency estimation with low computational complexity. Compared to the traditional fast Fourier transform (FFT) method, the FIIB achieves an improvement of 1.08 beats per minute (bpm). A reordering strategy is also introduced to mitigate potential misjudgments by FIIB. Key parameters of FIIB, including the number of frequency components L and the number of iterations Q, are analyzed and recommended. Dozens of subjects were recruited for experiments, and the root mean square error (RMSE) of heart rate estimation was less than 1.12 bpm on average at a distance of 1 m. Extensive experiments validate the high accuracy and robust performance of the proposed framework in heart rate estimation.

Synthetic Aperture Radar (SAR) imagery plays a vital role in maritime surveillance, yet it faces severe challenges such as speckle noise, cluttered backgrounds, and the small scale and low contrast of ship targets. To overcome these issues, we propose MBE-Net, a lightweight and high-accuracy SAR ship detection network featuring multi-branch edge-guided feature enhancement. Firstly, the Edge-Enhanced Wavelet Backbone (EEWB) is designed to enhance edge information and suppress detail loss by integrating multi-branch edge guidance and wavelet pooling, thereby improving small-ship perception in complex scenes. Secondly, the Lightweight Decoupled Detail-Enhanced Head (LD-DEH) decouples classification and regression tasks while embedding a detail-enhanced convolution module to recover high-resolution features, significantly boosting localization precision under strong interference. Lastly, the Adaptive Contextual Focus Feature Pyramid Network (ACF-FPN) introduces context-guided fusion to improve multi-scale feature alignment and robustness, especially in low-contrast and cluttered environments. Extensive experiments on the HRSID and SSDD datasets validate that MBE-Net achieves excellent detection accuracy with minimal computational cost, making it highly suitable for real-time SAR ship detection.