For the problems of multi-person vital sign detection at the same distance and extraction of heartbeat signals under the interference of respiratory higher harmonics, an algorithm based on digital beamforming and orthogonal projection filter (OPF ) is proposed. After the radar measures multiple baseband signals, digital beamforming is first performed to obtain the vital sign signals of people by generating multiple beams pointing to the subjects in space. Then, using the OPF, the obtained vital sign signals are projected onto the null space of their respective respiratory higher harmonics to achieve the removal of respiratory higher harmonic interference and the real-time acquisition of heartbeat signals. The results of multi-person experiments show that the proposed algorithm can successfully separate the detection of multi-person vital sign signals and the extraction of heartbeat signals, and the accuracy rate of heartbeat frequency estimation is as high as 99.68%.

In satellite-to-ground communication,during the process of P-band signal crossing the atmosphere to reach the ground array,its direction of arrival(DOA) and polarization angle are changed by ionosphere effects. Therefore,there is an error between the estimated values obtained by the existing DOA-polarization estimation algorithm and the real values. In order to solve the problem,corrections are made from the aspects of DOA and polarization angle. Firstly, considering the ionospheric refraction effect, a signal propagation model of ionosphere is established,based on which a correction method for pitch angle error is proposed. Secondly,regarding the Faraday rotation(FR) effect of ionosphere,the influence of Faraday rotation angle (FRA) on signal polarization is analyzed,and two estimation methods of FRA based on scattering matrix are proposed according to the process of ground target scattering. Simulation results demonstrate that the proposed methods can accurately calculate the correction value of pitch angle and FRA of P-band signal. Under certain conditions,the pitch angle estimation accuracy is improved by about 0.1°,and the FRA estimation accuracy is improved by nearly 1°,so the correction of DOA and polarization angle is realized.

For the problem that conventional signal analysis methods are prone to cause the“increasing batch”and“missing batch”in multi-functional radar signal sorting in complex electromagnetic environments, a multi-functional radar signal sorting method based on improved complex network community detection is proposed. This method first transforms the signal sequence into the complex network using limited penetration visibility graph. Then, it introduces spatial clustering with density to eliminate spurious pulses. Subsequently, the label propagation algorithm is improved according to the between centrality of nodes,enhancing the stability of community division. Finally,the sub-communities are merged to complete the signal sorting task by density peak clustering. Simulation results show that the proposed method achieves a sorting accuracy of 98.13% for multi-functional radar signals. Moreover,even when the proportion of spurious pulses increases to 35% , the number of sorted batches remains unchanged, effectively alleviating the“increasing batch”and“missing batch”problems.

To address the issue of synthetic aperture radar(SAR) images being susceptible to environmental noise,which leads to a reduction in the signal-to-noise ratio(SNR) at target locations,a method integrating singular value decomposition (SVD) and minimum entropy deconvolution (MED) to enhance the Back Projection(BP ) algorithm is proposed. Initially,the acquired echo signals undergo SVD, and a singular value matrix is obtained. By retaining only the first five singular values and reconstructing the echo signal matrix,an initial noise reduction is achieved. Subsequently,the signals are processed using MED filtering, where filter coefficients are iteratively updated to minimize the entropy of the signal,thereby reducing the kurtosis of the output signal and suppressing noise. A Zero-Phase Filter(ZPF) is then applied to restore any phase delays. Finally, the SAR image is generated using the BP algorithm. Experimental results demonstrate that this method significantly enhances the SNR at target locations,with improvements of 7.9 dB and 9.1 dB for large and small corner reflectors,respectively.

To solve the problem of signal aliasing after sampling in software defined radio (SDR ) , an improved phase-adjusted filtering algorithm is proposed. Based on the second-order RF bandpass sampling front-end with adjustable time delay,an anti-aliasing filter that supports multi-segment filtering is designed, and multiple frequency segments can be flexibly set according to the actual needs,so as to achieve accurate filtering for different frequency bands. Through the simulation verification in MATLAB SIMULINK, the method has better suppression performance than similar filters(38 dB or more) ,and can effectively filter out the required signal without affecting the integrity of other signals,which simplifies the receiving front-end,has strong flexibility and adaptability, and can better support future communication technology and high-density communication connections.

Object detection technology aims to locate and identify specific category targets in images or videos. However，in low-illumination scenarios，problems such as low contrast，blurred boundaries，and noise interference，result in the decline of detection performance. To address this，a Color Channel Transformation Enhancement-based Object Detection (C2TEOD ) algorithm is proposed. Firstly，a color channel transformation module is constructed，and learnable parameters are introduced to transform different color channels，enhancing the flexibility of the enhancement strategy. Then，an image enhancement module is employed to preprocess the input images. This module is jointly optimized with the object detection network using detection loss functions，thereby enabling the enhancement module to learn to generate representations that explicitly facilitate the subsequent detection task. Additionally，a selective self-supervised regression loss is proposed that uses both the original low-illumination images and the enhanced images as inputs to optimize the detection network. According to detection results，the enhancement module is further optimized through self-supervised regression to improve detection performance. Experimental results show that，compared with the baseline method，the mean average precision(mAP) metrics on the Exdark，M3FD，and LLVIP datasets are improved by 2.2%，1.1%，and 0.2% respectively.

Vehicular edge computing(VEC) converges the computing resources of cloud servers to the edge of the network closer to the vehicle side, allowing vehicles to offload vehicular computing tasks to the network edge servers,thus providing vehicles with low latency,high bandwidth and high reliability services. However,the highly dynamic network topology,strict low-delay constraints,and massive data of vehicular tasks of VEC pose significant challenges for implementing efficient offloading. The digital twin(DT)-driven VEC model can enable real-time monitoring of the state of the VEC network,thus assisting in making sound offloading decisions in the physical world. Firstly, the research progress of edge computing, available vehicles and DT-driven VEC task offloading methods are introduced. Then,the scenario architecture of DT-driven task offloading for VEC is elaborated. Finally,the future research challenges and solutions of DT-driven VEC task offloading methods are discussed,in hope of providing certain guidance for solving the problem of DT-driven VEC task offloading.

For the problem of target azimuth estimation under low signal-to-noise ratio (SNR) for active sonar in underwater environments,a direction of arrival(DOA) estimation multi-beamforming sonar imaging method based on fractional Fourier transform (FrFT )-enhanced iterative adaptive approach (IAA ) is proposed. Firstly, the echo signals received by hydrophones are subjected to FrFT preprocessing, transforming the wideband linear frequency modulation (LFM ) signals into narrowband signals in the fractional domain to avoid the influence of cross-interference terms. Then, focusing on LFM signals and suppressing noise in the FrFT domain is achieved. Finally,the iterative adaptive method is implemented in the FrFT domain,optimizing the power spectrum estimation method for accurate DOA estimation. Compared with traditional DOA estimation methods, the proposed method achieves better estimation accuracy and smaller root mean square error under low SNR conditions without increasing sensor array elements. It significantly improves imaging effectiveness, as indicated by simulation results showing sidelobe levels-13.364 dB for peak sidelobe ratio. in the range direction and -9.723 dB for integrated sidelobe ratio,-13.874 dB for peak sidelobe ratio in the azimuth direction and -10.034 dB for integrated sidelobe ratio.

Deep learning (DL) is an effective method for achieving automatic modulation identification (AMI) technology. However，DL methods generally struggle to balance recognition accuracy and efficiency simultaneously. To address this，a lightweight AMI method based on enhanced multi-scale feature fusion is proposed. First，a lightweight multi-scale feature fusion module is designed，which efficiently extracts multi-scale features of modulation signals through a cross-scale convolutional structure，enhancing the model's ability to represent different signal features. Next，an adaptive feature enhancement module is constructed，combining depthwise separable convolution and attention mechanisms to adaptively learn channel weights of key features，highlighting important signal features while reducing interference from irrelevant ones. Finally，a differential balance classifier is designed to focus on recognizing subtle modulation patterns，enabling efficient classification. Experimental results show that the proposed method improves recognition accuracy by an average of 5.91%，reduces the number of parameters by approximately 8.5×10⁵，and decreases iteration time per sample by 0.0624 seconds. Compared with the advanced models，it achieves higher accuracy，faster speed，and fewer parameters.

Traditional data stream clustering methods lack online dimensionality reduction capabilities for high-dimensional data, leading to limited clustering performance. To address this issue,a Scalable Subspace Learning for Clustering Data Streams(S²LCStream) method is proposed. Firstly,this method establishes a projection relationship between historical data and new data through scalable subspace learning,projecting the new data into the subspace spanned by historical data to obtain its clustering assignment in real-time. Secondly,to maintain the accuracy of clustering assignments over time, the method performs consistency detection of data distribution on the continuously arriving data stream,capturing concept drifts and adjusting clustering assignments through a backtracking mechanism to adapt to dynamically changing data distributions. Finally,the proposed method is validated on multiple real-world datasets, demonstrating its efficiency in handling high-dimensional data streams. Specifically, S²LCStream maintains high clustering accuracy while efficiently handling concept drift.