For the issue of decreased detection performance under low signal-to-noise ratio (SNR) conditions due to insufficient utilization of covariance matrix information in covariance-based eigenvalue algorithms for constructing detection statistics,a novel spectral sensing algorithm based on the ratio of the difference between the maximum and minimum eigenvalues to the harmonic mean of eigenvalues is proposed. This algorithm constructs the detection statistic by incorporating both the extreme eigenvalues and the harmonic mean of eigenvalues from the covariance matrix,thereby more comprehensively exploiting the eigenvalue information within the covariance matrix to enhance the detection capability. Furthermore, a novel approach for calculating the harmonic mean is introduced, leveraging the asymptotic distribution theory of eigenvalues in random matrices. This approach aims to not only improve the accuracy of the decision threshold but also further boost the detection performance. Simulation results demonstrate that the proposed algorithm,without requiring prior knowledge of primary users or channel conditions,achieves a detection probability increase of no less than 10% compared with several classic algorithms at -20 dB SNR.

Recently, with the development of deep learning, the field of lightweight object detection has witnessed significant progress. However, mainstream lightweight detectors ignore the extraction of multi-scale semantic information. In addition, these approaches ignore the relationship between deep semantic features and shallow detail features. To relieve above shortcomings, a Pyramid Pooling Enhanced Multi-scale Network(PPMENet) is proposed and an Efficient Pyramid Pooling Block (EPPB) is designed to extract multi-scale deep semantic information,strengthening the feature expression ability of the model. On the other hand, a Cross Semantic Level Interaction Attention Module (CSIAM) is designed to enhance information interaction between features at different semantic levels. Experimental results on the MS COCO 2017 test set show that PPMENet gets 28.0% average precision, only with 2.16×10⁶ model size and 0.97GFLOPs,and achieves inference speed of 218 frame/s. Compared with other methods, PPMENet realizes a good balance between detection accuracy and model execution efficiency.

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.

Multi-dimensional telemetry data pattern mining holds significant importance for satellite status monitoring. However, the sheer volume of telemetry parameters and data poses a challenge in obtaining precise solutions within a short timeframe. To address this issue,the authors propose a matrix profile-based pattern mining approach that employs stochastic principles to search for approximate solutions,which can serve as surrogates for precise solutions within an acceptable error margin. Firstly, spectral analysis is performed on the multi-dimensional telemetry data to determine the template length based on the characteristic frequencies of the patterns. Subsequently,the Mueen's algorithm for similarity search(MASS) is iteratively applied in a stochastic manner to compute elements within the distance matrix. A crucial step involves zeroing out elements near the main diagonal to form the multi-dimensional distance matrix. Finally, the minimum values are extracted from each column to generate the multi-dimensional distance matrix profile(MDMP ) . On this profile, the locations of the maximum and minimum values correspond to the identified rare and frequent patterns, respectively. Experimental analysis indicates that when processing three-dimensional telemetry data containing 150000 sampling points, the proposed method, at a 1% mining depth,is able to constrain the positional error between the approximate and precise solutions within 400 sampling points.

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.

To reduce the computations of parameters estimation in high-dynamic and long integration global navigation satellite system(GNSS) signal detection applications,the authors propose a low-computation GNSS acquisition method (LGAM) suitable for high-dynamic environment. The goal of LGAM is to apply the synthetic Doppler frequency hypothesis testing (SDHT) method to the acquisition of high dynamic GNSS signals with Doppler rate and bit flipping. Firstly,sparse Doppler frequency(SDF) process is implemented by coarse Doppler estimation,and post-correlation signal model is derived based on SDF structure. Then,in order to improve the detection efficiency of Doppler and Doppler rate, double-FFT based detection is proposed based on the post-correlation signal model for parameters estimation. The results demonstrate that in high dynamic environments, when the signal-to-noise ratio (SNR ) is higher than -43 dB, the computational complexity based on FFT method is 15 times that of LGAM1 and 780 times that of LGAM2.

The traditional antenna array wideband signal synthesis performance evaluation method has the problem of low signal synthesis performance evaluation accuracy due to the limited accuracy of the signal-to-noise ratio(SNR) estimation algorithm in the wideband and low SNR scenarios. For above problem, a wideband signal synthesis performance evaluation method of antenna array using power calculation is proposed. The method first simulates multiple intermediate frequency(IF) signals, applies time and phase delays to simulate the time delay and phase difference of the actual antenna received signals, and adds noise to each signal to simulate a low SNR environment. Then, the original and delayed signals are synchronously compensated until convergence using the antenna grouping algorithm to be evaluated. Finally, the synthesized power of the original signal after compensation is calculated and compared with the synthesized power of the ideal signal to obtain the synthesis loss. Simulation experiments results show that under the conditions of signal bandwidth of 250～500 MHz and SNR of -20～0 dB, the method has an improvement of about 1 dB in evaluation accuracy and 0.1 dB² in evaluation stability compared with the wideband signal synthesis performance evaluation method based on SNR, and the improvement effect is more significant with the decrease of signal bandwidth, and the improvement effect is more significant with the decrease of signal bandwidth.

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.

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.