Abstract:2025年26卷第3期目次

Abstract:“高速宽带航空通信关键技术专题”编者按

Abstract:In view of the problems that frequency band resources of aviation communication are limited, and aviation broadband communication system is inevitably interfered with narrowband communication system, a jamming detection algorithm is proposed in frequency domain based on the maximum posterior probability criterion to achieve accurate jamming suppression in combination with the frequency threshold excision algorithm. In this paper, the algorithm structure of the jamming detection algorithm based on the maximum posterior probability criterion is established, and the formula of the detection threshold is derived from the hypothesis testing theory. In addition, the performance of single carrier frequency domain equalization is compared to that of the orthogonal frequency division multiplexing under narrowband jamming, and the performance advantages of single carrier frequency domain equalization are explored, providing theoretical and experimental support for its potential application in broadband aviation communication. And then, the proposed jamming detection algorithm is applied to single carrier frequency domain equalization. The simulation results show that this algorithm achieves yet a lower error rate at both low and high jamming-to-signal ratio (-20~20 dB) in comparison with the traditional jamming detection algorithms based on mean, variance, and median under conditions of the interference bandwidth being occupied 20%, 10% and 5% in the signal bandwidth respectively.

Abstract:Aimed at the problems that transmission data is liable to make mistakes or to lose caused by fading and interference in high-speed aviation communication scenario, a new alternating direction method of multipliers decoder for non-binary low-density parity-check codes in Galois fields F2q is proposed. Firstly, a bit embedding rule is proposed, and the procedure of formulating the maximum likelihood decoding problem to a linear integer problem in real space is presented. Secondly, after relaxing the integer problem to a continuous one, an efficient ADMM algorithm is customized to solve the latter, where all the entries of the variable vectors can be obtained in parallel. Thirdly, it is important that the proposed ADMM decoder satisfies the needs of favorable codeword-independent property under some mild conditions and its computation complexity in each ADMM iteration is roughly O(nq). The simulation results show that this ADMM decoder is prior to the others in error-correction and decoding efficiency.

Abstract:In aeronautical communications, because of that data are imbalance, and being lack of minority class signal samples, there is a drop in the classifier performance in modulation signal classification tasks under complex electromagnetic environments, this paper proposes a classification method for unbalanced modulation signals based on deterministic oversampling. The method synthesizes minority class signal samples to balance the dataset and reduce the impact of data imbalance on classification performance. Based on the method at the RadioML 2016.10a dataset, 11 modulation types are selected under 5 signal-to-noise ratios (-8 dB, -4 dB, 0 dB, 4 dB, 8 dB) and 4 imbalance scenarios are constructed. The experimental results show that compared to the imbalanced dataset, the proposed method improves classification accuracy by 2.78%, 0.92% and 3.45% on MsmcNet, ResNet50, and DenseNet121 network models respectively, and compared to the traditional SMOTE method, the proposed method demonstrates still better performance in handling multi-class imbalance problems. And this method is enabled to effectively improve the accuracy in modulation signal classification in complex aeronautical communication environments, especially under complex electric-magnetic environments.

Abstract:Wideband signal detection framework enables to realize detection, identification, and time-frequency localization of multiple signals in the wideband RF systems with object detection being combined with spectrograms based on deep learning, whereas directly applied original network architecture is difficult to achieve optimal signal detection performance on actual task datasets. For the above-mentioned reasons, this paper proposes a network architecture, SignalNet, for voice signal detection task, which is decoupled for task-oriented optimization according to the characteristics of the voice signals and task dataset. Specifically, the backbone network is streamlined, which is responsible for feature extraction, a neck network that comprises the multi-scale time-frequency feature context fusion and gating attention modules is introduced, and the traditional anchor-based detection head is replaced with an anchor-free one. The experimental results show that the proposed network architecture achieves the optimal detection performance for the voice signal detection task, mAP reaches not only 97.42%, but also is in maintaining fewer model pa rameters and faster inference speed.

Abstract:In the air communication environment, especially in the air-to-air communication at the cruise phase of aircraft, serious multipath interference existed in channel may lead the received signals to become distortion or inter-symbol crosstalk, seriously affecting the communication quality. In consideration of that the residual inter-code of existing linear equalization algorithm seriously affects the equalization performance, resulting in error transmission errors, and the traditional MMSE-RISIC algorithm is limited by the accuracy of noise and decision to make the overall equalization performance limited, a decoding feedback equalization algorithm is proposed based on the residual estimation to reduce the impact of noise and inter-code interference on system performance. On the basis of MMSE equalization, first, the known sequence is utilized for estimating the noise, and then introducing a decoding interleaving module to improve the decision reliability. Finally, the verdict signal is used to estimate the residual inter-symbol interference. The simulation results show that under the 16QAM modulation, the decoding feedback equalization algorithm based on residual estimation has 0.5 dB error performance improvement compared with the traditional MMSE equalization algorithm in aviation communication channel environment.

Abstract:In order to explore all the possibilities of civil cargo transport aircraft for loading military equipment, improve the ability to respond quickly to the needs of operations, and provide the necessary military equipment and various types of protective materials for operational success, weight and balance problems of civil-to-military transport aircraft are studied. To determine the loading position and orientation of military equipment in the cargo hold of an aircraft, the civil wide-body freight aircraft B747-400F modified model is selected as the research object. An optimization model that integrates a two-dimensional geometric model with the load balance of transport aircraft is established by utilizing a two-stage decomposition strategy based on the two-dimensional cutting theory in consideration of the cargo hold of the transport aircraft as a rectangular plate, and military equipment as multiple rectangular items with varying lengths and widths. In the two-dimensional geometric position constraint model, constraints are considered for the non-overlapping positions of military equipment, maintaining a certain distance, not exceeding the cargo hold boundaries, and the possibility of orthogonal rotation. In the load balance optimization model, the objective function is to minimize the center of gravity (CG) deviation and maximize the payload in consideration of constraints such as the transport aircraft’s weight, payload, CG, cargo hold cumulative load, linear load, and area load. A Benders decomposition algorithm is designed to decompose the load balance optimization model with complex issues containment into a master problem in position allocation and a subproblem for weight balance checking. Gurobi is used to solve two example scenarios for model verification. The results indicate that the established model and the algorithm can quickly effectively determine the loading position and the orientation of military equipment under a reasonable CG, improving the payload, and providing references and insights for intelligent loading.

Abstract:In this paper focuses on crack propagation research in fatigue-critical regions such as hole edges and R-zones at stiffened panels. A simulation-based processing method for crack propagation across boundary regions is proposed, along with a Bayesian-updating-based dynamic fatigue crack propagation life prediction method. Based on the Pairs crack propagation rate model in combination with Abaqus/Zencrack, a finite element model of a scaled stiffened panel is simulated. The simulation results and the experimental data are input into a sample dataset, and a neural network is used to build a fatigue crack propagation parameter database. Dynamic Bayesian network (DBN) inference is adopted to construct a fatigue crack propagation life prediction model suitable for stiffened panels. Fatigue crack propagation experiments are made on the stiffened panel structure under condition of constant amplitude loading are also conducted. The results show that the proposed simulation processing method for crack propagation across boundar regions is enabled to effectively ensure the continuity of simulation data. The Bayesian-updating-based dynamic fatigue crack propagation life prediction method can effectively correct deviations between the simulation data and the experimental data, generating predictions still more close to the actual crack propagation behavior.

Abstract:In view of weaponry systems, and in consideration of complex and multidimensional characteristics in weaponry systems for a synthetic brigade, a contribution evaluation method is proposed for a synthetic brigade in weaponry systems based on super-network. Firstly, a super-network model of weaponry systems for the synthetic brigade is constructed, and the weapon nodes and nodes’ elationships in the systems are modeled respectively based on their functions and types of interactive information, and then, the combat capability of the weaponry system is measured by using the information support capability, command decision-making capability and firepower strike capability, the emergence of weaponry systems is evaluated by using the number of operation loops included in the combat network, and an index is established based on the capability of weaponry systems and the numbers of operation loop. Finally, taking the firepower strike at the enemy command center by a certain synthetic brigade as an example, the contribution rate of FPV drone to the weaponry systems is analyzed, and the feasibility of the method is verified, providing a theoretical support for the optimization configuration and development in weaponry systems to a synthetic brigade.

Abstract:Mixed reality, as a new human-computer interaction method, has broad application prospects, but whether the mixed reality technology can effectively improve the human-computer effectiveness level in command system remains to be researched. In order to effectively evaluate the human-computer effectiveness level of mixed reality command interface in consideration of the limitations in existing human-computer interface evaluation methods, a multi-indicator comprehensive evaluation method is proposed by designing experiments to compare the task performance, physiological responses, and subjective feelings between the traditional two-dimensional and mixed reality display and interaction systems. By collecting task completion time, eye movement data, physiological indicators, EEG data, and subjective ratings, normalization and weighted processing of both objective and subjective data are carried out to achieve quantitative evaluation of the interface human-computer effectiveness of the two human-computer interaction systems. The multi-indicator comprehensive evaluation method in evaluating the mixed reality human-computer in terface is valid, providing an experimental basis and theoretical support for the scientific evaluation and optimization of the mixed reality command system human-computer interface.

Abstract:To characterize the effect of process variation of interconnect lines on single event crosstalk (SEC), Based on the equivalent RLC model for the interconnect line and the equivalent circuit for single event transient (SET), the ultimate process corner parameters of single event crosstalk (SEC) consideration with the process variations of the line are achieved by analyzing extreme difference and variance on the results of the designed orthogonal experimental, and then the mechanism of the effects of echnology node, ion energy, length of line on the ultimate process corner of SEC are also discussed. The results show that under condition of the combined effects of the coupling effects between the interconnect lines and the pulse propagation characteristics, and 45 nm above technology node, when the interconnect structure parameter fluctuates ±10%, the variation range of SEC is greater than 20%, and the relative variation increases with the increase of the current amplitude. Whereas there is no significant difference with the increase of the interconnect length. Under condition of 45 nm below technology node, although the voltage peak and noise area of SEC increase significantly, there is a decrease in the influence of interconnect process fluctuation on SEC, and the fluctuation of SEC increases with the increase of interconnect length.

Abstract:Aimed at the problems that reconnaissance-strike UAV is complex and diverse in combat tasks, difficult to obtain test data, and strong subjectivity in safety evaluation, an improved Bayesian network model is proposed to evaluate the deploying safety of the reconnaissance-strike UAV. According to the operational characteristics of the reconnaissance-strike UAV, five operational stages are divided, the safety risk factors in the use of different stages are identified, and the deploying safety evaluation index system of the reconnaissance-strike UAV in the strong confrontation environment is constructed. Based on the index system, the topological structure of Bayesian network is determined. The entropy method is utilized for improving the G1 method to obtain the prior probability of the root node, and the EM algorithm for obtaining the conditional probability of the child node. Finally, the data is imported into the GeNIe software for simulation, and the probability distribution of different use safety levels of econnaissance-strike UAV is obtained. The reverse reasoning of the model is carried out to identify the key factors that lead to the safe ty accidents of the reconnaissance-strike UAV.

Abstract:Recognition of air target intent occupies a position of strategic importance in the realm of battlefield situational awareness. Nonetheless, how to quickly and accurately extract pertinent information from extensive situational data is still a question in this domain. The majority of prevalent research models are characterized by intricate architectures, hindering the efficient inference of target intentions within a concise timeframe. For the above-mentioned reasons, a model is introduced based on Transformer architecture. The model is optimized by Reverse method to adapt it further to handle time-series tasks. And, the integration of perturbation elements merged into the position encoding elevates the model’s robustness and generalization capabilities. Additionally, this paper implements lightweight enhancements to both the attention mechanism and the feedforward neural network. By a comprehensive evaluation encompassing comparative experiments, ablation studies, and an in-depth analysis of computational complexity, the efficacy of the proposed model is unequivocally substantiated within the domain of airborne target intent recognition.

Abstract:Infrared target detection is a commonly used means to UAV countermeasure technology. Aimed at the problems that infrared small target image is not obvious in feature and is often submerged in noise under conditions of complex environments, an improved YOLOv8 target detection algorithm is proposed. Firstly, the introduction of an attention mechanism into research is utilized for adaptively adjusting the size of the receptive field. Secondly, a small target detection layer is constructed to pay more attention to the shallow information of the network, enhancing the ability of finegrained feature extraction. Finally, the detection head improved by depth separable convolution is used to improve the detection accuracy and to simultaneously become even more lightweight. The experimental results show that the precision rate, recall rate, mAP50 and mAP50-95 are improved by 5.3%, 8.1%, 9.1% and 21.1% respectively in comparison with the original YOLOv8 algorithm. The result comes off well in the detection of small targets by UAV.

Abstract:In view of the truncated window causing the performance loss by the conventional sliding window decoding (SWD) algorithm to spatially coupled LDPC (SC-LDPC) codes, a SWD algorithm is proposed based on window extension (ESWD) in this paper to improve the reliabilities of the information in the decoding window, upgrading further the whole decoding performances. On this basis, a low latency with early termination window sliding scheme is proposed to reduce the number of sliding, realizing the good trade-off between the decoding performance and decoding latency. The simulation results show that the proposed ESWD algorithm has about 1.8 dB gain with decoding window size being small, compared with the conventional SWD algorithm. And with the increment of window size, though the performance gain decreases gradually, the performance is still superior to that of the sliding window decoding algorism at close decoding performance of the sub-optimal belief propagation algorithm. Moreover, the decoding complexity and latency expressions are also derived. The analysis results show that compared with the conventional SWD algorithm, although the slight increase of the decoding complexity in one decoding window is resulted by the additional few check nodes due to the window extension in the proposed ESWD algorithm, the whole decoding complexity keeps almost the same with the conventional SWD algorithm and the decoding latency is significantly reduced.

Abstract:To address the problem of insufficient transferability of adversarial examples and inadequate black-box attack capabilities in deep learning models, this study designs an iterative fast gradient method based on the NadaMax optimizer (NM-FGSM). This method integrates the advantages of Nesterov Accelerated Gradient and the Adamax optimizer, improving the accuracy of gradient updates through adaptive learning rates and lookahead momentum vectors. Additionally, dynamic regularization is introduced to enhance the convexity of the problem, optimizing algorithm stability and specificity. The experimental results demonstrate that the NM-FGSM is prior to the existing methods under conditions of various attack strategies, particularly in advanced defense scenarios, attack success rate increases by 4%~8%. The dynamically regularized loss function enhances the cross-model transferability of adversarial examples, thereby further improving black-box attack effectiveness. Finally, points out the way forward for the NMFGSM algorithm and defense measures, providing a new insight into the security research of deep learning models.