For target tracking and localization in bearing-only sensor network, it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation. This paper proposes a distributed state estimation method based on two-layer factor graph. Firstly, the measurement model of the bearing-only sensor network is constructed, and by investigating the observability and the Cramer-Rao lower bound of the system model, the preconditions are analyzed. Subsequently, the location factor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation. Building upon this foundation, the mechanism for propagating confidence messages within the fusion factor graph is designed, and is extended to the entire sensor network to achieve global state estimation. Finally, groups of simulation experiments are conducted to compare and analyze the results, which verifies the rationality, effectiveness, and superiority of the proposed method.

When performing tasks, unmanned clusters often face a variety of strategy choices. One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effectiveness under the condition of strategic diversity. This paper analyzes these task requirements from three perspectives: the diversity of the decision space, information network construction, and the autonomous collaboration mechanism. Then, this paper proposes a method for solving the problem of strategy selection diversity under two network structures. Next, this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolution dynamics model for unmanned cluster strategy in the context of strategy selection diversity according to various unmanned cluster application scenarios. Finally, this paper provides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolution in autonomous cluster collaboration for the two types of models. On this basis, this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks, thereby providing decision support for practical applications of unmanned cluster tasks.

The belief rule-based (BRB) system has been popular in complexity system modeling due to its good interpretability. However, the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability. The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by integrating accuracy and interpretability into an optimization objective. But the integration has a greater impact on optimization results with strong subjectivity. Thus, a multi-objective optimization framework in the modeling of BRB systems with interpretability-accuracy trade-off is proposed in this paper. Firstly, complexity and accuracy are taken as two independent optimization goals, and uniformity as a constraint to give the mathematical description. Secondly, a classical multi-objective optimization algorithm, nondominated sorting genetic algorithm II (NSGA-II), is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity. Finally, a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization. The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization, and has capability of joint optimizing the structure and parameters of BRB systems with interpretability-accuracy trade-off.

In the existing impact time control guidance (ITCG) laws for moving-targets, the effects of time-varying velocity caused by aerodynamics and gravity cannot be effectively considered. Therefore, an ITCG with field-of-view (FOV) constraints based on biased proportional navigation guidance (PNG) is developed in this paper. The remaining flight time (time-to-go) estimation method is derived considering aerodynamic force and gravity. The number of differential equations is reduced and the integration step is increased by changing the integral variable, which makes it possible to obtain time-to-go through integration. An impact time controller with FOV constraints is proposed by analyzing the influence of the biased term on time-to-go and FOV constraint. Then, numerical simulations are performed to verify the correctness and superiority of the method.

Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure, thus gaining an advantage in a war. However, the existing cascading failure modeling analysis of interdependent networks is insufficient for describing the load characteristics and dependencies of subnetworks, and it is difficult to use for modeling and failure analysis of power-combat (P-C) coupling networks. This paper considers the physical characteristics of the two subnetworks and studies the mechanism of fault propagation between subnetworks and across systems. Then the survivability of the coupled network is evaluated. Firstly, an integrated modeling approach for the combat system and power system is predicted based on interdependent network theory. A heterogeneous one-way interdependent network model based on probability dependence is constructed. Secondly, using the operation loop theory, a load-capacity model based on combat-loop betweenness is proposed, and the cascade failure model of the P-C coupling system is investigated from three perspectives: initial capacity, allocation strategy, and failure mechanism. Thirdly, survivability indexes based on load loss rate and network survival rate are proposed. Finally, the P-C coupling system is constructed based on the IEEE 118-bus system to demonstrate the proposed method.

This paper proposes a unified dynamic modelling method for the rigid-flexible coupled robots that have variable topology. Furthermore, a global simulation method covering all the phases during the transformation from one topology to the another one, is also put forward. The proposed modelling and simulation method can be applied for both single and multi-robots transformation for vast on-orbit manipulation missions. First, a configuration parameter is presented that contains vital information on the topology configuration. An autonomous generation method of topology configuration description matrix for multi-arm spacecraft is also proposed. Secondly, a unified recursive kinematic modelling method and a general rigid-flexible coupling dynamic modelling methodology based on spatial operator algebra are described using configuration parameters. Then, a global simulation method that can be applied in both single and multi-robots operation scenario is presented to deal with abrupt changes in the state of motion. Finally, several topology simulations for rigid-flexible coupling multi-arm space robots are presented to verify the proposed method. These simulation results show that the methods can deal with several different topology scenarios while avoiding constraint violation problems.

With the popularization of social media, public opinion information on emergencies spreads rapidly on the Internet, the impact of negative public opinions on an event has become more significant. Based on the organizational form of public opinion information, the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emergency network. The emotion recognition model of negative public opinion information based on the bi-directional long short-term memory (BiLSTM) network is studied in the model layer design, and a linear discriminant analysis (LDA) topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to realize further in-depth analysis of information topics. Focusing on public health emergencies, knowledge acquisition and knowledge processing of public opinion information are conducted, and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events, thus demonstrating important research significance for reducing online public opinion risks.

To overcome the limitations of traditional force aggregation methods, this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer (SATC-ALO) and the self-organizing map (SOM) network. The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences, adaptive tent chaos search, tournament selection, and logistic chaos sequences. Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network. The model is resolved using SATC-ALO and SOM independently, with experiments demonstrating that SOM achieves fast and accurate grouping, while SATC-ALO offers higher precision but requires longer computational runtime, making it more suitable for hybrid approaches. Both methods are validated as practical solutions for force aggregation tasks.

Automatically recognizing radar emitters from complex electromagnetic environments is important but non-trivial. Moreover, the changing electromagnetic environment results in inconsistent signal distribution in the real world, which makes the existing approaches perform poorly for recognition tasks in different scenes. In this paper, we propose a domain generalization framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments. Specifically, we propose an end-to-end denoising based domain-invariant radar emitter recognition network (DDIRNet) consisting of a denoising model and a domain invariant representation learning model (IRLM), which mutually benefit from each other. For the signal denoising model, a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model. For the domain invariant representation learning model, contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distribution. Moreover, we design a data augmentation method that improves the diversity of signal data for training. Extensive experiments on classification have shown that DDIRNet achieves up to 6.4% improvement compared with the state-of-the-art radar emitter recognition methods. The proposed method provides a promising direction to solve the radar emitter signal recognition problem.

The survivability of the infrastructure network, especially the combat system, is a core index to reflect the merits of the system. The cascade effect caused by the failure of a single or a few nodes is the focus of the research on the resistance. Many scholars have studied this problem and put forward some useful cascading failure models. Considering node resilience, a cascade failure model is proposed in which nodes have three states: normal, overload and failure. In addition, the conditions for the failure of connected edges are given. The model fully considers the redundancy design of the real system, and can objectively reflect the performance of the system to deal with cascade failure. We study the effect of node resilience on network cascade failure in both typical network and real network. Experimental results show that the proposed model can reduce the scale of cascade failures with higher cost utilization compared with the classical model that is widely used, especially when the network capacity is small.

In order to provide low Earth orbit (LEO) satellite network survivability with limited resources, and effectively respond to the risk of on orbit operation, a survivable routing algorithm based on multi-agent reinforcement learning is proposed. The reinforcement survivable routing establishes the reward function from the perspective of network utility, and describes the improvement of survivability in the failure scenario as a joint optimization problem, which solves the online routing decision-making problems after LEO satellite network failure. Each cognitive user agent and satellite agent can independently determine the routing strategy for communication missions according to the time slot topology, node survival status, storage space and other environment information of the satellite network. The simulation results show that compared to the weighted semi-distributed routing algorithm (WSDRA) and Fibonacci multipath load balancing (FMLB) algorithm, the proposed multi-agent double deep Q-learning network (MADDQN) reinforcement survivable routing algorithm can adapt to satellite failure and topology changes, and improve network utility by 14% and 9% respectively in human attack scenario, and by 17% and 13% respectively in random failure scenario.

For air-to-air missiles, the terminal guidance’s precision is directly contingent upon the tracking capabilities of the roll-pitch seeker. This paper presents a combined non-singular fast terminal sliding mode control method, aimed at resolving the frame control problem of roll-pitch seeker tracking high maneuvering target. The sliding mode surface is structured around the principle of segmentation, which enables the control system’s rapid attainment of the zero point and ensure global fast convergence. The system’s state is more swiftly converged to the sliding mode surface through an improved adaptive fast dual power reaching law. Utilizing an extended state observer, the overall disturbance is both identified and compensated. The validation of the system’s stability and its convergence within a finite-time is grounded in Lyapunov’s stability criteria. The performance of the introduced control method is confirmed through roll-pitch seeker tracking control simulation. Data analysis reveals that newly proposed control technique significantly outperforms existing sliding mode control methods by rapidly converging the frame to the target angle, reduce the tracking error of the detector for the target, and bolster tracking precision of the roll-pitch seeker huring disturbed conditions.

With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile warehouses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to minimize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution augmented large neighborhood search (MEALNS) algorithm incorporating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.

This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external disturbances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising during measurements, thereby enhancing the robustness and stability of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference signals utilizing local information and communication with neighbors. Subsequently, a fixed-time sliding mode controller is introduced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve distributed average tracking of reference signals, and rigorous analytical methods are employed to substantiate the fixed-time stability. Finally, numerical simulation results are provided to validate the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.

This paper addresses the problem of designing distributed cooperative guidance laws for multiple missiles in three-dimensional (3D) space, considering the constraint of simultaneous impact and fully intermittent communication. The problem is structured using a 3D nonlinear mathematical model along with a topology network for interaction. By employing the relative distance and velocity leading angle as coordination variables, a novel finite-time cooperative guidance law is developed through an event-triggered strategy, which eliminates the need for time-to-go estimation. The law’s convergence is demonstrated through finite-time Lyapunov stability theory. Additionally, the guidance approach also eliminates Zeno behavior, which refers to infinite communication instances within a finite time interval, ensuring fully intermittent communication among the missiles. To prevent singularity caused by non-zero velocity leading angles in cooperative guidance, the proportional navigation guidance (PNG) law is applied when coordination variables reach consensus and missiles are nearing the target, which can guarantee that all missiles strike the target simultaneously. Simulation results verify that the developed law achieves impact time correction within a finite duration while maintaining fully intermittent communication throughout the process.

A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method, based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with $n $=10 subcarriers and a bit error rate of 1×10^?5, spectral efficiency can be raised by roughly 12.4%.

Accurate target angle estimation is one of the challenges for wideband radars due to the fact that target occupies multiple range bins, resulting in lower energy or signal to noise ratio in a single range bin. This paper proposes a processing technique for enhanced accuracy of target angle estimates for wideband monopulse radars. Firstly, to accumulate the energy of the received echo signals from different scatterers on a target, the phase difference between different scatterers on a target is estimated using the minimum entropy phase estimation method combining with the correlation between adjacent pulses. Then, the monopulse ratio is obtained by using the signals from the accumulated sum and difference channels. The target angle is estimated by weighting the accumulated echo energy for accuracy enhancement. Experimental results based on both numerical simulation and measured data are presented to validate the effectiveness of the proposed technique.

This paper presents a fixed-time cooperative guidance method with impact angle constraints for multiple flight vehicles (MFV) to address the challenges of intercepting large maneuvering targets with difficulty and low precision. A cooperative guidance model is proposed, transforming the cooperative interception problem into a consensus problem based on the remaining flight time of the flight vehicles. First, the impact angle constraint is converted into the line of sight (LOS) angle constraint, and a new fixed-time convergent non-singular terminal sliding surface is introduced, which resolves the singularity issue of the traditional sliding surfaces. With this approach, LOS angle rate and normal overloads can converge in fixed time, ensuring that the upper bound of the system convergence time is not affected by the initial value of the system. Furthermore, the maneuvering movement of the target is considered as a system disturbance, and an extended state observer is employed to estimate and compensate for it in the guidance law. Lastly, by applying consensus theory and distributed communication topology, the remaining flight time of each flight vehicle is synchronized to ensure that they intercept the target simultaneously with different impact angles. Simulation experiments are conducted to validate the effectiveness of the proposed cooperative interception and guidance method.

Imagery modeling for microwave engineering generally requires a large number of training samples to generate images. In this paper, a variational autoencoder (VAE) with the multilayer perceptron (MLP) is proposed for efficient imagery modeling of electromagnetic behaviors of the microwave antenna and component. The input of VAE is the randomly generated binary images of a physical structure, and VAE is an unsupervised learning scheme. The training of MLP needs the labeled samples of the latent representation of VAE and the electromagnetic responses. Compared with supervised imagery modeling, VAE generates a large number of low-cost images applied to the image processing section. The implementation of unsupervised learning eliminates the need for labeled samples from full-wave simulations, resulting in significant time saving. The proposed model is confirmed with two examples of a pixel antenna and a microstrip/coplanar waveguide ultrawideband filter, and the results show its improvement in accuracy and efficiency.

To address the significant overhead of the beam training in millimeter wave (mmWave) wireless communications, we propose a balanced multimodal fusion network with bi-directional enhancement in the previous work. It can predict the optimal mmWave beam according to the mmWave wide beam and sub-6 GHz channel state information. Utilizing the interaction and complementarity between different modes can improve the accuracy and robustness of predictions compared to the single mode (mmWave only or sub-6 GHz only). However, the feature extraction of one modality can potentially impact other modalities via parameter backpropagation. Although multimodal models perform better than unimodal models, they may be underutilized. To solve the problem, the adaptive multimodal gradient (AMMG) optimization method is designed to update the gradient of per feature extraction branch adaptively in this paper. Furthermore, a multimodal bi-directional enhancement (MBdE) block is proposed to combine features of other modalities, enhancing the complementarity of mmWave modality and sub-6 GHz modality. As revealed by the numerical results, it is evident that the proposed scheme delivers more precise outcomes with lower overhead compared to conventional and modern deep learning methods.

A wideband low-profile magneto-electric (ME) dipole antenna is proposed in this paper. In order to reduce the profile of ME-dipole antenna, a planar wideband balun with metallic holes connection is proposed to replace the formal feeding structures. Four resonant modes are excited in the ME-dipole for wide impedance matching bandwidth. By combing the wideband balun with multi-mode ME-dipole, an impedance bandwidth of 81.3% (2.7−6.4 GHz) for voltage standing wave ratio (VSWR) less than 2.5 is achieved. This antenna structure is designed based on the multi-layer printed circuit board (PCB) technology, with the advantage of low cost and easy to fabricate. The overall size of the fabricated antenna is (0.5×0.5×0.061)λ_L (λ_L is the wavelength of low frequency). The radiation patterns of this antenna are measured with an average gain of 5.3 dB in each resonant modes. In addition, the average radiation efficiency of this antenna is more than 75%, and the cross polarization is lower than −20 dB over the whole frequency band.

This paper proposes a hybrid differential evolution and ant colony optimization (DE-ACO) algorithm for multi-unmanned aerial vehicle (UAV) task allocation and path planning, addressing dynamic task allocation in heterogeneous UAV clusters. The algorithm integrates task order constraints, uniform target point weights, and path planning to avoid terrain obstacles and radar threats, ensuring efficient resource utilization. Simulations demonstrate balanced task allocation, rapid convergence, and robustness under UAV failure and communication range reduction scenarios. Compared to a baseline ant colony optimization approach, the hybrid DE-ACO method offers enhanced flexibility, making it a promising solution for real-world multi-UAV missions.

Threat assessment is vital for air defense of warship, as it significantly improves decision making. Aiming at the problem that the uncertainty and combat intention of the target is not considered in the process of threat assessment for air defense of warship, a threat assessment method based on the variable weights intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is proposed. Considering the inherent attributes and spatial situation of air targets, the threat assessment index system is established. Aiming at the uncertainty problem in the process of threat assessment, the method of transforming indexes into intuitionistic fuzzy sets (IFSs) is proposed. The objective weights and subjective weights are calculated based on intuitionistic fuzzy entropy (IFE) and combat intention, respectively. By minimizing the distance to the objectivity and subjectivity simultaneously, the calculation method of comprehensive variable weights based on the least square is proposed. An example show that the proposed method can give accurate threat degree and ranking of targets. Compared with other algorithms, it is shown that the proposed method can fully exploit the influence of the inherent attributes and spatial situation and give more reasonable results according to the target combat intention.

With the continuous improvement of 5G network infrastructure, the informatization process in the industrial sector is accelerating at an unprecedented pace. The market demand for gateways integrating 5G networks with multi-access edge computing (MEC) technology is growing increasingly strong. There is also a rising requirement for these gateways to possess capabilities such as high reliability, large bandwidth, fast processing speed, and localized data processing. This paper, in response to the practical management needs of photovoltaic power stations and industrial fields, proposes the design of a 5G artificial intelligence (AI) edge computing intelligent gateway with dual 5G modules. The design is based on the i.MX8M Plus processor and integrates dual 5G module design. It supports multiple communication modes, including 5G, Wi-Fi, Ethernet, and serial ports. The software incorporates edge computing scripts, various communication protocols, and a lightweight AI model, enabling real-time data acquisition and processing at the edge. It supports for several industrial standard protocols ultra-low latency acquisition, and intelligent operation features like remote over-the-air upgrade, secure shell secure operation, and configuration hot updates. The system was tested and validated in a typical photovoltaic application scenario. The results demonstrate that, compared with the traditional single-module Internet of Things gateways, the dual 5G AI edge gateway proposed in this paper reduces end-to-end latency by more than 70%, improves data reliability tenfold, enhances bandwidth by 100%–200%, extends coverage capability by 50%, effectively alleviates the bandwidth pressure on the core network, and enhances the overall reliability and security of the system.

Unmanned platforms, performing various tasks in urban environments, have garnered significant attention. This paper primarily focuses on analyzing and modeling the influence of various urban environmental factors on the unmanned platforms’ core competencies, which include motion, perception, operation, and communication abilities. Initially, the paper delves into the urban environment’s geographical, meteorological, electromagnetic, and temporal factors, identifying and categorizing the typical elements into static and dynamic components. It further models the primary urban elements and elucidates their interrelationships. Then, the paper categorizes the types of unmanned platforms that are suitable for urban task execution. It identifies six fundamental components essential for describing the capabilities of these platforms: the perception, computing, motion, power, operation, and communication systems. Subsequently, the paper constructs an impact model by analyzing how typical urban environmental factors affect these six components. A unified analysis framework is proposed to assess the urban environment’s impact on the capabilities of unmanned platforms. Finally, the effectiveness of this framework is validated through path-planning tasks in various typical urban scenarios.

Previous studies have paid limited attention to the critical role of unmanned aerial vehicle (UAV) altitude variation in determining three-dimensional (3D) search efficiency. To address this gap, this paper introduces a deep reinforcement learning framework integrated with adaptive matrix optimization in multi-UAV target search. The proposed framework explicitply models altitude variations’ impacts on observation range and detection accuracy. To balance flight safety, coverage, and detection precision, deep Q-network (DQN)-altitude matrix optimization (AMO) discretizes 3D trajectories into horizontal paths and vertical altitude decisions, effectively reducing the problem dimensionality. Furthermore, a curriculum learning approach is employed to decompose the search process into phased sub-tasks, each with customized decision rules and reward mechanisms. This hierarchical strategy accelerates agent learning and enhances performance in complex scenarios. Comprehensive experimental evaluations in simulated environments demonstrate that the proposed DQN-AMO outperforms benchmark methods in both robustness and generalization.

The radar cross section (RCS) scale transformation of non-metallic targets is mainly limited by the frequency dispersion relationship of material, making it difficult to be implemented in actual measurements. If the scale dependences of scattering centers can be found to avoid the influence of frequency, it is expected to provide a new solution for the scale measurement of RCS of non-metallic targets. Therefore, based on the high-frequency asymptotic method, this paper first derives the scale dependences of the specular reflection mechanism and the edge diffraction mechanism for three common specular reflection structures and two edge diffraction structures. Then, referring to the expression of the frequency dependent factor in the geometric theory of diffraction (GTD) model, the scale dependent factors of the above scattering mechanisms are analogously proposed. The scale dependent factors are extended to thin-layer coated structures and arbitrary multiple scattering cases. Simulation experiments verified the correctness of the scale dependent factors. Finally, this paper presents an extended expression form of the GTD model, laying a theoretical foundation for the subsequent RCS scale transformation of non-metallic targets based on scattering centers.

Deep learning has achieved excellent results in various tasks in the field of computer vision, especially in fine-grained visual categorization. It aims to distinguish the subordinate categories of the label-level categories. Due to high intra-class variances and high inter-class similarity, the fine-grained visual categorization is extremely challenging. This paper first briefly introduces and analyzes the related public datasets. After that, some of the latest methods are reviewed. Based on the feature types, the feature processing methods, and the overall structure used in the model, we divide them into three types of methods: methods based on general convolutional neural network (CNN) and strong supervision of parts, methods based on single feature processing, and methods based on multiple feature processing. Most methods of the first type have a relatively simple structure, which is the result of the initial research. The methods of the other two types include models that have special structures and training processes, which are helpful to obtain discriminative features. We conduct a specific analysis on several methods with high accuracy on public datasets. In addition, we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power. In terms of technology, the extraction of the subtle feature information with the burgeoning vision transformer (ViT) network is also an important research direction.

Non-terrestrial network (NTN) possesses ubiquitous coverage and access capability, which needs high-reliability and high-throughput transmission supported by the advanced channel coding. In this paper, a cooperative transmission and optimization scheme is proposed based on polar codes in NTN. Firstly, the transmission system model is designed. Then the polar coded cooperation method among the cooperative satellite nodes (CSN) and destination is provided in Rayleigh slow fading channel. Moreover, to improve the overall error probability performance and throughput efficiency, the key coding rate and power allocation of CSN are jointly optimized using multiobjective reinforcement learning algorithm. Finally, simulation and analysis demonstrate the validity of our approach and show the error-correcting performance and throughput advantages of the proposed cooperative transmission scheme in NTN.

International freedom of the air (traffic rights) is a key resource for airlines to carry out international air transport business. An efficient and reasonable traffic right resource allocation within a country between airlines can affect the quality of a country’s participation in international air transport. In this paper, a multi-objective mixed-integer programming model for traffic rights resource allocation is developed to minimize passenger travel mileages and maximize the number of traffic rights resources allocated to hub airports and competitive carriers. A hybrid heuristic algorithm combining the genetic algorithm and the variable neighborhood search is devised to solve the model. The results show that the optimal allocation scheme aligns with the principle of fairness, indicating that the proposed model can play a certain guiding role in and provide an innovative perspective on traffic rights resource allocation in various countries.

Direction-of-arrival (DOA) estimation provides the angle information about interesting targets for array radar systems. However, conventional algorithms are designed for line-of-sight (LOS) scenarios and thus cannot be used to estimate non-LOS (NLOS) targets. Recently, intelligent reflecting surface (IRS) has been applied to DOA estimation for NLOS environments. This paper proposes a geometric model utilizing IRS for DOA estimation in NLOS scenarios. First, we establish an IRS-aided DOA estimation framework, where one-pair-IRSs is used to manipulate the propagation direction of the detection signals. Then, we formulate the estimation task using the elliptic positioning technique. Subsequently, we derive a closed-form solution for the resultant problem. Furthermore, we improve the architecture using multi-pair-IRSs and then design a binary weighting method to enhance the accuracy of DOA estimation. In comparison with the existing methods, the proposed algorithm is able to estimate two-dimensional (2D) DOA for NLOS targets. Numerical simulations are conducted to validate the effectiveness of IRS for DOA estimation in NLOS environments.

The missiles typically require multiple stages tests to improve hit accuracy. The existing estimation methods seldom consider the tests characteristics of multiple stages growth of missile hit accuracy, which bring difficulties to accurately estimate the missile hit accuracy. Considering the different degrees of damage caused by missile hitting the target in different areas, the Dirichlet distribution is selected as the prior distribution of hit accuracy parameters based on the Bayesian method. The sequence constraint relationship between the hit accuracy parameters of each stage test is established, and the Bayesian estimation model of hit accuracy based on multiple stages growth tests is constructed. The Markov chain-Monte Carlo method combined with Gibbs sampling is used to overcome the problem of solving the posterior high-dimensional integral of the model to complete the model solution. The example shows that this method can fuse multiple stages growth tests information compared with the existing single stage test method, which can provide reference for the estimation of missile hit accuracy during the period of research and development.

Aerial gravity measurements in polar regions require higher accuracy and stability from satellite navigation systems. The performance of conventional Global Positioning System (GPS) significantly deteriorates as one moves from lower latitudes to polar areas. First, a regional comparative analysis of BeiDou Navigation Satellite System (BDS) and GPS positioning performance in polar regions is conducted. Results indicate that in polar environments, BDS demonstrates certain advantages regarding satellite availability and geometric distribution. Based on this analysis, a method for extracting airborne gravity anomalies using BeiDou signals in polar regions is proposed and improved. The process begins by optimizing BeiDou observation data using an altitude angle weighting method combined with precise ephemeris data. An error correction model is constructed to enhance system adaptability in the polar environment. Following this, high-precision position and acceleration estimation is achieved using the precise point positioning (PPP) technique, integrated with inertial navigation information, to extract aerial gravity anomalies in polar regions accurately. Results from Antarctic aerial gravity measurement experiments indicate that the BDS-based model improves accuracy by 30% compared to GPS under the same conditions.

Flight mission segments are essential components of aero-engine flight mission profiles. Current recognition methods rely on manual processes, leading to inefficiencies and inaccuracies in aero-engine load analysis. To solve this problem, this paper presents an automatic method for recognizing flight mission segments using bidirectional recurrent neural network (Bi-RNN). Initially, the decomposition of flight mission profiles, manual division of mission segments, labeling of mission segment, and the establishment of maneuver sequence table are introduced, respectively. Secondly, the automatic recognition models are constructed using bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU). By collecting measured load data from a third-generation aero-engine, including 50 low maneuvering and 50 complex maneuvering flight mission profiles, the dataset is set utilizing K-fold cross-validation and the sliding window method. The optimal parameters for the models are determined through training, and their recognition performance is assessed. The results indicate that the Bi-RNN model achieves over 80% accuracy in recognizing flight mission segments in both low and complex maneuvering flight mission profiles. As a result, the flight mission segment automatic recognition method presented in this study allows for more efficient and precise aero-engine load analysis, which is of great significance for aero-engine life evaluation research.

This paper proposes a two-dimensional (2D) angular super-resolution framework for sparse arrays under the single snapshot condition. The azimuth-elevation 2D angular super-resolution model is established, which shows the relationship between the 2D angular super-resolution image and the signal. Using this model, the 2D angular super-resolution problem is transformed into the beyond linear optimization problem. In order to efficiently and accurately address this optimization problem, we propose the sparse multi-layer iterative (SMLI) algorithm based on beyond linear signal processing (BLiSP) theory. During the layered iterative process, the solution region is continuously narrowed. The constructed nonlinear weighting matrix and sparse constraint coefficient enhance the ability to differentiate between the effect of signal and noise in solving the beyond linear optimization problem. Additionally, the nonlinear weighting matrix can be adaptively updated during the solving process, ensuring high-performance angular super-resolution results under different signal-to-noise ratios (SNRs). Experiments confirm the proposed method’s effectiveness and robustness.

With the transformation of user demands in knowledge economy era, traditional knowledge service operation paradigm and implementation countermeasures cannot meet the demand of users. The previous research on knowledge service system is rich in theoretical content, but most of the research only stays on the shallow analysis of the concept and features. There is lack of information available to the construction of knowledge service system framework in details, especially in the academic area. This paper first contributes to the existing body of knowledge service solution, and then combines general knowledge service solution with integrated definition function (IDEF0) models to produce a knowledge service system framework. These IDEF0 models depict the hierarchy within knowledge service purposes and reveal strategies of meeting user demands in academic research. Finally, a prototype system in knowledge service system shows that the proposed framework can provide effective knowledge service for users, which will help to promote quality and satisfaction in knowledge service process.