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.

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.

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.

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.

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.

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.

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% .

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.

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.