It's difficult to implement maintainability design and evaluation effectively in the early stages of development of space TT&C ground system. To solve this problem,a maintainability design and evaluation system is constructed based on virtual reality technology,a maintainability design and evaluation workflow is developed,and a comprehensive evaluation criteria for maintainability design is proposed. In the virtual maintenance scenario,virtual maintenance prototypes and virtual maintenance resources are used to verify the overall process,and immersive simulation is conducted to verify the accessibility,visibility,and human body comfort of the maintenance of components of a vehicle mounted space TT&C ground system. Thus the quantitative comprehensive evaluation of virtual maintainability design is achieved. The results show that this method reduces costs by 68% , shortens duration by 61% compared with traditional method. It may serve as a practical reference for visualizing,quantifying carrying out maintainability design and evaluation in the early stages of development of space TT&C ground system.

In broadband reconnaissance scenarios，achieving high signal detection accuracy often entails significant computational costs. To address this，a multi-scale convolution attention sparse detection(MSCAS) method is proposed，which incorporates prior knowledge of signal spectrograms by capturing long-range temporal dependencies and suppressing irrelevant frequency-domain interference. MSCA-S introduces a multiscale horizontal convolution attention(MSHCA) mechanism that jointly extracts multi-dimensional signal features，enhancing detection accuracy while reducing computational complexity through horizontal convolution. Building on MSHCA，a hierarchically stacked broadband signal detection framework is developed，and sparse feature parameters are used to further optimize computational efficiency. MSCA-S is evaluated on a real-world and simulated broadband signal dataset(2.5 MHz spectrum) collected in Qingdao，achieving an average detection accuracy of 95.6% across varying signal-to-noise ratios. Compared with the frequency-sensitive signal detector，the Swin-Transformer-based protocol recognition method，and the Res-101 detection method，MSCA-S improves accuracy by 0.05%，2.94%，and 6.14%，respectively，while reducing computational costs by 1.53×10¹⁰，1.79×10¹⁰，and 4.59×10¹⁰，respectively.

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.

In order to achieve low-latency and low-energy offshore communication, the dynamic service cache update mechanism is introduced into the complex neural network, and the mobile edge dynamic service caching policy (MEDSCP ) based on double deep Q network (DDQN ) is proposed by cleverly designing the complex neural network structure based on offshore communication scenarios. The policy firstly obtains the optimal offloading decision set through the user terminal task offloading decision game, and then utilizes mobile edge computing(MEC) and dynamic service caching update to reduce the delay and energy cost of task execution in the offshore communication environment, aiming to improve the efficiency of task processing in offshore communication and to expand the development potential of this industry. Simulation experimental results show that the proposed MEDSCP strategy can achieve fast convergence of the algorithm while guaranteeing the training effect,and also effectively reduce the delay-energy weighted sum of offshore communications compared with existing work.

For the optimal feature subset selection and model parameter optimization in ultra-wideband non-line-of-sight(NLOS) recognition，a new NLOS recognition method based on the cross-validation recursive feature elimination algorithm of Light Gradient Boosting Machine(LightGBM) and Optuna parameter tuning is proposed. First，six important features，including the difference between the first path signal and the total received signal power，and the maximum noise，are selected as the optimal feature subset using the recursive feature elimination and cross-validation algorithm. Then，Optuna is used to optimize the hyperparameters of LightGBM model. Line-of-sight and non-line-of-sight feature data is collected，and the Support Vector Machine，Extreme Gradient Boosting algorithm，and parameter-optimized LightGBM model are trained and tested. The results demonstrate that the selected features exhibit excellent discriminative ability，with the optimized LightGBM model achieving a recognition accuracy of 95.28% .

In view of the problems of false detection and missed detection when an unmanned aerial vehicle (UAV) detects targets at different scales,a YOLOv8-FDT UAV algorithm model with a multi-scale fusion mechanism is proposed. First, a dynamic upsampling module is added to the Neck layer of the baseline model to reduce the number of model parameters and improve the real-time performance of the model for target recognition. In addition, in order to enable the entire algorithm model to capture different scale semantic information of the target in the feature fusion stage,adaptive downsampling and depth convolution are integrated to design the feature diffusion pyramid network(FDPN). Finally,experiments on the UAV aerial photography dataset VisDrone2019 show that the mean average precision(mAP) of all categories of the improved model is increased by 6.24% compared with that of the baseline model.

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.

In order to further improve the accuracy of human action recognition and fully explore the spatiotemporal features of action sequences, a graph convolution action recognition method based on spatiotemporal feature fusion and attention mechanism is proposed. The spatial attention map convolution is used to refine the topology to capture the correlation features of the joints under different motion types,and the time convolution structure is extended by the time domain multi-scale convolution module to capture the multi-scale time features. A multi-level feature fusion module is constructed,which takes the initial feature and the convolution output feature of the time-domain multiscale graph as the module input,and uses a two-branch structure to obtain the global and local channel features respectively. On this basis,a limb attention mechanism is proposed to divide the human topological structure and calculate the attention weights in the channel dimension respectively to enhance the model's ability to pay attention to local action features. The experimental results show that the recognition accuracy is 93.0% and 96.9% in CS and CV evaluation mode of NTU RGB+D data set,and 89.8% and 91.1% in X-Sub and X-Set evaluation mode of NTU RGB+D 120 data set,respectively. The recognition accuracy is higher than that of ST-GCN,CTR-GCN and other models.

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 order to reduce complexity of construction of indoor positioning fingerprint database and improve the positioning accuracy, an indoor fingerprint positioning algorithm based on matrix completion under the 5G ultra-dense network is proposed. In the offline database construction stage,the algorithm first uses the K-nearest Neighbor(KNN) interpolation method to complete the matrix of part of the fingerprint database to construct a complete database. Secondly,the sparse auto-encoder is used to extract the sparse features of the fingerprint database, and the high-dimensional received signal strength indication (RSSI) signal is reduced. In the online fingerprint matching stage,the weighted KNN algorithm is used to estimate the coordinates of the point to be located. After experimental simulation,the average relative error of the algorithm to reconstruct the fingerprint database is 0.31% . Compared with that of the traditional KNN fingerprint matching algorithm,the average error is reduced by 24.41% .

In response to challenges in sea clutter modeling within the classical algorithms,including the lack of fitting accuracy due to the inability to satisfy multiple statistical characteristics simultaneously and the limitations in controllably generating accurate class-based results,combining the generative power of U-Net with the potential of complex-valued neural networks to deal with complex nonlinear problems in the electromagnetic domain, a novel approach is proposed. This approach integrates complex-valued network layers and a classifier-free guidance module, establishing an interpretable mapping mechanism for input conditions,resulting in complex-valued guided diffusion model(CVG-DM). This model is centered on the direct utilization of the complex-valued baseband signals from the In-phase and Quadrature(IQ) path of sea clutter, as well as the exploration of the relationship between sea clutter and strong targets in the background. This enables controlled generation of the model under varying conditions of target presence or absence, and assessment based on amplitude distribution, temporal and spatial correlation, nonlinear characteristics,and Doppler spectrum. Simulation experiment validates CVG-DM's capability in realizing sea clutter data augmentation under varying conditions. The simulated clutter can simultaneously take into account above five statistical properties, surpassing the completeness of real number network-based evaluation metrics and further enhancing fidelity.

In satellite communication systems operating in dynamic interference environments,the quality of channels and the interference power vary. Limited spectrum resources and complex interference environments pose challenges for anti-interference communication decisions, particularly in terms of resource allocation and service demands. Specifically, the challenge lies in efficiently utilizing resources while avoiding interference frequencies and optimizing power. To address this issue,a deep reinforcement learning-based anti-interference algorithm with multiple reward functions is proposed. The algorithm models the interaction between the transmitter,receiver,and interferer as a Markov decision process. By optimizing the reward function associated with the costs of channel and power switching,it introduces mechanisms for both frequency and power switching,analyzes the interference characteristics in the spectrum of adjacent time slots, and integrates the interference signal features collected during the interaction with channel information to train an anti-interference strategy. This strategy enables joint anti-interference decision-making in both the frequency and power domains. Simulation results demonstrate that the algorithm effectively reduces the probability of interference,accelerates convergence,and optimizes the utilization of power resources.

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.

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.

For the problems of limited expression of characteristic information and low classification accuracy in radiation source classification tasks,an individual radiation source recognition method based on multi-resolution feature fusion is proposed. In this method, the individual characteristics of the radiation source are expressed by using three time-frequency spectra with different resolutions obtained through the Short-Time Fourier Transform. Multi-channel convolutional neural networks are constructed using ResNext50 to extract features with different time-frequency resolutions. A multi-channel feature weighted fusion mechanism is introduced into the network,and the features of different channels are fused by feature weighted fusion, combining the feature information from different resolutions. Experiments show that this method improves the ability to express the subtle fingerprint information of the radiation source signal,and compared with that of the feature layer fusion method and the single feature expression method, the recognition accuracy is improved by 2.15% and 6.8% ,respectively.

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.

For the problem that the existing deep learning modulation recognition algorithms are not robust enough and have insufficient generalization ability in complex signal environments，a multi-channel network based on phase parameter estimation and spatial reconstruction(PET-SAMCL) is proposed. First，the input in-phase quadature(IQ) signal is converted by phase parameter estimation and divided into three modules to extract the amplitude-phase feature，IQ combination and branching features of IQ respectively. A spatial reorganization unit(SRU) is added to the feature extraction module to reduce the influence of redundant features. The spatial features are refined and fused by global average pooling and soft attention operations，and the temporal and spatial features are extracted by gated recurrent units(GRU) and bidirectional gated recurrent units(BiGRU) . Ablation study determines the optimal model structure. The model performs well on the RML2016.10a dataset，achieving a maximum recognition accuracy of 93.9% at 14 dB，and the average recognition rate is increased by 7.7% compared with that of other models.

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.

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.

Automatic Dependent Surveillance-Broadcast IN(ADS-B IN) applications can provide numerous conveniences for pilots during flight operations, with safety being the prerequisite for realizing the advantages of ADS-B IN applications. A Bow-tie model-based safety assessment method is introduced to address potential safety issues associated with ADS-B IN applications in actual flight activities. By analyzing pre-defined hazards,the maximum acceptable probability of hazard occurrence is determined,which in turn leads to the derivation of safety requirements such as the failure rate of ADS-B equipment or the integrity of communication data links necessary to achieve this probability. Based on the explanation of this method,a specific implementation case is presented to further illustrate its application.

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.

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.

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.

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.

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%.

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.

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.