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.