Highlights What are the main findings? Across five communication-navigation scenarios, QA-DSE reduces position RMSE over Covariance Intersection by 74.08-74.24% and stays within 8.1% of an idealized no-cooperation Kalman-filter floor, while CI and Equal-Weight fusion incur a 4 & times; degradation relative to that floor. The algorithm is mean-square bounded, exponentially convergent, and BIBO stable, with per-step cost measured at 0.09-0.31 ms per UAV for fleet sizes from 5 to 50. What are the implications of the main findings? Under the UWB-style cooperative-observation model used here, where each neighbor's transmitted GPS noise sets the pseudo-observation error budget, the principal benefit of quality-aware fusion is graceful degradation toward the single-vehicle floor rather than improvement above it. A dual-constraint AND-gate provides hard exclusion under joint AoI-covariance degradation (Theorem 5), complemented by soft attenuation of single-axis faults through the multiplicative quality metric (Theorem 4).Highlights What are the main findings? Across five communication-navigation scenarios, QA-DSE reduces position RMSE over Covariance Intersection by 74.08-74.24% and stays within 8.1% of an idealized no-cooperation Kalman-filter floor, while CI and Equal-Weight fusion incur a 4 & times; degradation relative to that floor. The algorithm is mean-square bounded, exponentially convergent, and BIBO stable, with per-step cost measured at 0.09-0.31 ms per UAV for fleet sizes from 5 to 50. What are the implications of the main findings? Under the UWB-style cooperative-observation model used here, where each neighbor's transmitted GPS noise sets the pseudo-observation error budget, the principal benefit of quality-aware fusion is graceful degradation toward the single-vehicle floor rather than improvement above it. A dual-constraint AND-gate provides hard exclusion under joint AoI-covariance degradation (Theorem 5), complemented by soft attenuation of single-axis faults through the multiplicative quality metric (Theorem 4).Abstract Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors-freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)-into a single multiplicative score qij, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, On3+nm per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI-covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08-74. 24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04-74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work.

The threat posed by unauthorized drones to public airspace has become increasingly critical. To address the challenge of UAV detection in unaligned visible–infrared dual-spectral images, we present a novel framework that comprises two sequential stages: image alignment and object detection. The Speeded-Up Robust Features (SURF) algorithm is applied for feature matching, combined with the gray centroid method to remove mismatched feature points. A plane-adaptive pixel remapping algorithm is further developed to achieve images fusion. In addition, an enhanced YOLOv11 model with a modified loss function is employed to achieve robust object detection in the fused images. Experimental results demonstrate that the proposed method enables precise pixel-level dual-spectrum fusion and reliable UAV detection under diverse and complex conditions.

The rapid proliferation of low-altitude unmanned aerial vehicle (UAV) applications has made autonomous identification technology critical for flight safety and collaborative operations. In this paper, we propose and systematically analyze an autonomous identification scheme based on Bluetooth Low Energy (BLE) technology. We formulate a comprehensive system model that integrates link budget, packet collision, identification success probability, and power consumption. By incorporating safety interval constraints and a three-channel integrated reception probability, we employ an exhaustive search algorithm to optimize monitoring strategy parameters, thereby achieving an optimal trade-off between the Recognition Success Rate (RSR) and power consumption. Simulation results indicate that, at a PHY 1 Mbps rate, the optimal monitoring strategy theoretically approaches the Target Level of Safety (TLS) requirements for civil UAVs under the defined model assumptions, with a power consumption of 19.24 mW and an Average First Identification Delay (AFID) of 105 ms. Furthermore, simulation analysis verifies the scheme’s feasibility under dynamic topology, interference, and multi-UAV scenarios, providing a solid theoretical and technical reference for the practical implementation of autonomous UAV identification.

To address the problem of three-dimensional trajectory prediction for aircraft in high-density airspace and tackle the critical limitations of traditional particle filter algorithms in terms of stability and efficiency, this paper proposes a Grey Relational Analysis-Enhanced Particle Filter (GRA-EPF) algorithm. Through innovative approaches such as dynamic particle number adjustment, resampling strategy optimization, and weight coefficient determination based on grey relational analysis, the algorithm improves prediction accuracy and robustness. Experimental results demonstrate that the proposed GRA-EPF algorithm significantly enhances the fit between predicted results and actual trajectories, substantially reduces position error and root mean square error (RMSE), and exhibits certain advantages in prediction accuracy.

By 2030, projected UAV operations may exceed one million concurrent flights in urban airspace, yet traditional fixed-distance separation methods fail to accommodate heterogeneous platforms. This paper introduces a three-tier hierarchical dynamic separation framework adapting minima across strategic (30–80 m category-specific baselines), pre-tactical (0.7–1.8× encounter-dependent scaling), and tactical (real-time 3D decomposition) timescales. Monte Carlo simulation across 100,000+ flight hours demonstrates 47% collision rate reduction versus fixed 30 m separation (0.008 vs. 0.015 per 1000 h, p < 0.001), 50% airspace utilization increase (18.4 vs. 12.3 UAVs/km3), 44% flight time penalty decrease (8.5% vs. 15.2%), and 99.97% ICAO-compliant TLS achievement (≤10−7 per flight hour) with real-time performance (78.5 ms for 20 UAVs). The framework provides an immediately deployable foundation for heterogeneous UAV traffic management.

This study proposes an enhanced convolutional neural network (CNN)-based task scheduling framework for multi-UAV cooperative networks engaged in joint target search and tracking missions. To address the computational complexity inherent in traditional tree search methods for task allocation, we develop a novel neural-augmented optimization architecture that integrates CNN-based node cost prediction with systematic search space pruning. The core innovation lies in employing deep convolutional networks to estimate the minimum cost bounds of decision nodes, enabling efficient pruning of non-optimal branches in the search tree while preserving optimality guarantees. Furthermore, we establish a comprehensive simulation environment incorporating various state-of-the-art artificial intelligence algorithms for performance benchmarking. Simulation evaluations confirm the superior computational efficiency of our CNN-augmented scheduling framework when benchmarked against conventional branch-and-bound methods, while simultaneously demonstrating marked improvements in mission success rates compared to contemporary reinforcement learning-based schedulers. The proposed methodology provides a viable solution for real-time mission planning in dynamic multi-UAV systems, effectively balancing computational efficiency with effectiveness.

With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability.

With the further opening of the national low-altitude airspace, Unmanned Aerial Vehicles (UAVs) will be used in large numbers and at high density in urban low-altitude airspaces. Currently, there is a lack of accurate and efficient methods for high-density UAV conflict detection. To address this issue, this paper constructs a UAV protection zone model and a battlefield airspace grid model to extract the airspace position matrix of each UAV. By employing the Hadamard product calculation method for matrices and the properties of composite and prime factorization, the method detects UAV conflicts, identifying the UAV numbers, positions, altitudes, and other information of conflicts, enabling real-time and rapid detection of conflicts among a large number of UAVs in the airspace. The results show that compared with traditional methods, this method can detect all conflicts with a single mathematical calculation when the number of UAVs is large, with the conflict detection time for 300 UAVs controlled within 1ms, significantly improving the efficiency of conflict detection.

This paper presents a conflict detection and resolution model for unmanned aerial vehicles (UAVs) based on the principle of prime factorization. The goal is to achieve autonomous path planning and conflict management in a decentralized flight architecture. The model draws on the widely used prime factorization method in informatics and cryptography, applying the process of decomposing integers into prime products to UAV flight control. Based on the gridded airspace represented as a matrix, the process of integer decomposition into prime factors is utilized in UAV flight control, which possesses a certain degree of innovation. Through simulation experiments in complex air traffic environments, the model's effectiveness in identifying potential conflicts and dynamically adjusting flight altitude was validated. Results indicate that the model can ensure flight safety and efficiency in most cases and demonstrates good scalability, maintaining efficient operation even with a significant increase in the number of UAVs. This study provides a promising solution for decentralized UAV flight control, although it remains in the preliminary stages. Further research and optimization are expected to expand the scope of autonomous UAV flight. Future work will focus on improving algorithm efficiency, optimizing flight strategies, and conducting broader testing and validation in real-world environments.

With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existing working mode of simply using computer mapping and manually finding airspace conflict contradictions can no longer meet the large-scale air use demand. In response to the existing spatial representation model of longitude and latitude grid, which has large grid deformation in high latitude areas, and the problem of slow computation speed of the conflict detection (CD) algorithm that determines whether the airspace boundary coordinates overlap, we propose a grid model that represents airspace with a spherical rhombic discrete grid of positive icosahedron and design a matrix-based digital representation method of airspace, which uses matrix product operation. The matrix product operation is used to quickly determine whether there is a conflict between airspace and airspace and between airspace and routes.

针对现有的以经纬度为网格剖分的空域表征模型,在高纬度地区网格形变较大,且以空域边界坐标判定空域之间是否重合的冲突检测算法存在的计算速度慢的问题,提出以正二十面体球面菱形离散格网大圆弧剖分为基础,用全等菱形离散格网表征空域,结合空域优先级,利用多层级希尔伯特(Hilbert)空间填充曲线对空域进行统一编码.设计了基于矩阵的空域数字化表征方法,利用哈达玛积(Hadamard)乘积运算快速判定多个空域之间的用空属性是否存在冲突.仿真结果表明:该方法具有较高的网格精度,实现秒级冲突检测,与传统冲突检测算法相比,能够达到降低算法运算量,提高运算速度的目的.

为了快速检测空域冲突、及时进行冲突消解,针对现有空域冲突检测算法计算量大、速度慢问题,设计了一种空域冲突检测算法.该算法采用正二十面体剖分方式,将空域格网化;结合矩阵运算,大大降低了空域冲突检测运算量.该算法在大规模空域冲突检测时能够快速、准确地检测出空域冲突位置和冲突空域编号.

为防止低空无人机(UAV)冲突解脱过程中发生危险接近或事故,将该过程的安全问题转化为控制问题,提出基于STPA-TOPAZ的低空无人机冲突解脱安全性分析方法.首先基于系统理论的事故模型和过程(STAMP),构建冲突解脱系统中的安全控制结构.然后利用系统理论过程分析(STPA)根据系统运行的上下文信息确定系统级事故和危险,识别出冲突解脱过程中的不安全控制行为,并分析产生不安全控制行为的关键致因.最后利用TOPAZ方法定量描述致因因素对系统安全的影响程度,找到制约系统安全的瓶颈.仿真结果表明了 STPA-TOPAZ方法的有效性与优越性.

塔台飞行管制员的指挥能力直接影响航空安全和空域资源利用率.塔台指挥模拟训练作为快速提升塔台飞行管制员指挥能力的有效途径,训练效果评判难是制约其训练效率的重要因素.研究塔台模拟训练系统的智能化辅助评估功能,开展评估基本规则制定、指标设计、平台设计和评估结果界面设计,期望为塔台指挥模拟训练系统智能辅助评估功能提供参考依据,优化模拟训练效果评估手段,将教员从传统的随堂逐人、逐系统跟训跟评的模式中解脱出来,提高训练考评效率.

In order to improve the safety management level of military aviation management. On the basis of SPA theory, this paper proposes a safety evaluation method for military aviation management. In this method, according to the operational characteristics of military aviation management system, an evaluation index system for military aviation management safety is established. On this basis, the concepts of set pair and connection degree in SPA theory are introduced to conduct safety evaluation research on military aviation management.

In order to solve the unfair individual payment costs problem in the low-altitude unmanned aerial vehicle (UAV) conflict resolution process, a multi-UAV conflict resolution algorithm based on the cooperative game concept “coalition complaint value” is proposed. Firstly, based on the low-altitude multi-UAV conflict scene characteristics, according to the “coalition complaint value” concept, the UAV conflict resolution payment matrix is established. Secondly, combined with the advantages of the artificial potential field (APF) method and the genetic algorithm (GA), a hybrid solution strategy for conflict resolution based on APF-GA is proposed. The final simulation results show that the APF-GA hybrid solution strategy has the best efficiency by combining the three evaluation indicators of calculation time, feasibility, and system efficiency. The reliability of the proposed algorithm is verified based on the Monte Carlo algorithm. The solution strategy based on the cooperative game “coalition complaint value” can improve individual fairness to a certain extent. At the same time, it can achieve the rapid planning goal with priority drones at the expense of a small amount of overall benefits.

A novel photonic scheme to generate microwave frequency shift keying (FSK) signals with high frequency multiplication factors or flexible tuning carrier frequencies is proposed. A parallel structure is constructed in which the upper arm contains a dual parallel Mach–Zehnder modulator (DPMZM) while the lower one contains another DPMZM and a phase modulator (PM). From a theoretical analysis, the two DPMZMs can generate signals containing specific optical sidebands by properly biased, which serve as optical sidebands selector. The PM driven by a binary coding signal is employed to control the phase difference between the two arms. Simulation results show that when the output signal from the parallel structure is directly sent into a photodetector (PD), microwave FSK signals with frequency multiplication factors of 2/4 or 4/8 can be obtained. And when the output signal is mixed with a tunable optical source before being sent into the PD, microwave FSK signals with flexible tuning carrier frequencies can be obtained. The proposed scheme can generate two major categories of microwave FSK signal. Furthermore, polarization independence and good frequency tunability can be simultaneously achieved because no polarization multiplexing devices or frequency-dependent devices are applied. The impacts of phase deviation of phase shifter and non-ideal extinction radio of modulator are discussed.

Abstract Starting from the reality of airlines, this paper studies the operational risk and control methods of airlines, establishes relevant risk index system, and formulates corresponding control methods. Based on the four aspects of human, aircraft, environment and management, the risk index system is established. The weight of the index and the score of each system are determined by using the analytic hierarchy process and the fuzzy comprehensive evaluation respectively, and the airline risk evaluation model is established. In the formulation of airline management and control methods, genetic algorithm is used to compare different flight task combinations, and the advantages of this method are obtained.

针对单一干扰方式在实际作战中的不足,提出了一种复合干扰方式,对其进行了必要性和可行性分析,介绍了复合干扰的具体原理,以功率准则和效率准则对其干扰效能进行了仿真分析,结果表明,复合干扰方式信干比大于单一无源干扰信干比,被干扰雷达的最大探测距离和箔条云与雷达的距离、箔条云与干扰机的距离密切相关,在近地突击和纵深打击等近距作战任务中,实施复合干扰比单一有源干扰更具优势.最后,探讨了复合干扰在其他几种战术中的应用.

Electronic Jamming is an important means to cover the penetration formation and improve survival probability of penetration aircraft when Air force performs ground penetrating mission. A compound interference method of jamming surface-to-air missile guidance radar by scattering long–range jamming signals though chaff clouds is proposed, which uses jammer to irradiate chaff clouds scattered on the penetration route, and injects jamming energy into the main lobe of surface-to-air missile guidance radar by scattering chaff clouds to make better interference effect.