The multi-body dynamics in the launch process of a space platform deploying a server, as well as the optimal double impulse rendezvous guidance law between the server and the target spacecraft, are studied. Firstly, the space platform enters into orbit around the target, keeping its launch tube axis aiming at it. After receiving the launch command, the server shoots out from the launch tube, flying to the target. Due to body coupling, the platform’s attitude is disturbed, preventing the server from accurately aiming at the target during separation. The server uses its small rocket engine to apply two velocity pulses: the first one to adjust its trajectory for rendezvous, and the second near the target to reduce relative velocity to zero for soft docking. A two-body dynamics model is established using the Newton-Euler method, and a virtual prototype is developed in ADAMS for validation. To solve the multi-objective optimization subject to energy consumption and flight time for rendezvous, an improved non-dominated sorting genetic algorithm II (NSGA-II) algorithm is proposed. Simulation results show that launch-induced perturbations are non-negligible, and the proposed algorithm effectively derives the optimal guidance law that balances energy use and flight time.

In this paper, we propose evaluation approaches for the spatial target localization precision based on the observation matrix conditional number. Three evaluation approaches for the spatial target localization precision are derived including relative condition numbers, absolute condition numbers, and volume condition numbers by properties of vectors and matrix norms. The theoretical analysis shows that the proposed methods are the upper certainty bound of the magnification of measurement error. Meanwhile, the proposed methods are able to account for variations of localization accuracy by exploiting geometric variations in the composition between the target and measurement stations. Finally, the proposed methods perform better, compared with the traditional evaluation methods. This is of great significance for the accuracy evaluation of high-precision measuring equipment, optimization of workstation layout and geometric configuration. Simulation experiments corroborate the effectiveness of the proposed methods.

A dual-frequency aperiodic planar scanning array with diversified radiation elements is proposed. The proposed element consists of an elliptic patch and two parasitic rectangular patches, which can work both at 5.28?5.33 GHz with difference radiation beam and 5.78?5.83 GHz with sum radiation beam. A four equivalent magnetic currents model has been established to explain the radiation principle of the proposed element. Compared to the beamwidth of the sum beam, the 3 dB beamwidth of the difference beam is broadened about 30% and the radiation gain of the sum beam is obviously improved about 48%. A 64-element aperiodic array with diversified radiation elements is constructed. The array can scan at a wide angle of ±70° with no grating lobes at difference radiation mode and can scan in the range of ±60° with high gain at sum radiation mode.

The implementation of timely monitoring and preventive maintenance plays a fundamental role to ensure the reliable operation of complex systems. Condition-based maintenance strategy offers an effective means to leverage system remaining life information, enabling the application of targeted measures to reduce maintenance costs and elevate overall operational efficiency. This study delves into a performance degradation system affected by external random shocks, utilizing the Wiener process model to characterize the continuous degradation process. Within this framework, two distinct condition-based monitoring schemes are proposed: one is the real-time condition monitoring and the other is the dynamic periodic monitoring. Through the optimization of maintenance strategies for each scheme based on the long-term average cost, the study aims to optimize the preventive maintenance threshold for system failure. The Monte Carlo simulation algorithm is adopted to solve the optimization problem. Finally, a comprehensive numerical example is provided to validate the efficiency of both the models and the proposed maintenance strategies.

To address the energy consumption issues caused by task lengths in task scheduling on heterogeneous multi-core systems, this paper proposes an adaptive parameterized improved simulated annealing algorithm based on the directed acyclic graph task model. The algorithm employs feedback from acceptance rates to dynamically adjust the temperature and neighborhood size of the simulated annealing process. Additionally, it introduces a security mechanism to enhance convergence speed and global search capabilities. Compared against classical simulated annealing and standard heuristic algorithms, the proposed algorithm achieves reductions exceeding 54% in convergence generations, 50% in task slots, and 10% in scheduling time, providing a direction for low-power task scheduling.

Validation for simulation models often confronts challenges with small samples due to the costs of time and money. To address this issue, this paper presents a validation method for small-sample dynamic outputs based on Gaussian process regression (GPR) models. Firstly, a validation framework based on Bayes statistics is proposed, shifting the focus from merely analyzing validation data to a more comprehensive analysis of posterior distributions. Subsequently, the posterior distributions of both the simulation outputs and the reference data are separately captured through segmented GPR. Then, the consistency of these posterior distributions is evaluated in terms of the central tendency and the distribution range. This consistency serves as a quantitative measure of the simulation model’s credibility, expressed as a value ranging from 0 to 1, where a value closer to 1 indicates higher credibility. Finally, the effectiveness of this validation method is demonstrated through a numerical example and an application example, highlighting its capability in uncertainty description and adaptability to small samples.

Remote sensing satellites (RSS) are highly complex and customized from a common product family. It makes the traditional model-based system engineering (MBSE) method, which lacks architecture-level reusability, difficult to apply to their architecture design. Considering RSS has a relatively fixed common architecture from which the various design solutions are customized specific to different missions, the model-based product line engineering (MBPLE) methodology can be leveraged. In this paper, based on the analysis of the current RSS development process in practice and the main issues of current MBPLE methods, an RSS-specific MBPLE approach is proposed. Firstly, the RSS domain terminology is consolidated into the RSS-MBPLE SysML profile to support the construction of models in the design process. Then, the two key steps, i.e., architecture configuration and standalone product selection are efficiently conducted following the MBPLE principles supported by plugins of the mainstream SysML platform. Finally, a typical RSS control subsystem is illustrated as the case study to demonstrate the effectiveness of the proposed method. The results show that the proposed approach improves architecture-level reusability and design automation compared with traditional methodology, thereby reducing manual clone-and-modify efforts and enhancing the efficiency of RSS architecture design.

Accurately sensing the channel state of heterogeneous networks is key to matching users’ diverse service communication demands with the channel state, and is an effective way to improve the utilization efficiency of network resource. However, existing channel state perception methods are not suitable for heterogeneous network, and their perception performance is easily affected by interference uncertainty. In order to achieve channel state perception of heterogeneous networks, this paper adopts a centralized collaborative perception model, where each node obtains local channel state perception results based on statistical pulse parameters at the physical layer. In order to reduce the impact of interference on perception performance, this paper uses the Jousselme distance to quantify the degree of difference among nodes caused by interference. Using the average credibility as a threshold, nodes in the sensing area are classified. On this basis, the local perception results of each node are performed classification-based correction to improve the accuracy and reliability of channel state perception. Simulation results indicate that the proposed method has good adaptability for channel state perception in complex electromagnetic environments. The perception results can accurately reflect the actual channel state, which is conducive to improving the network throughput.

To predict the ranking of the country’s innovation capability in the world in real-time, this study designs a two-phased prediction model based on the pairwise comparison. Data from the global innovation index (GII) reports are employed in this study. Countries with different income levels have shown different development inertias, the two-phased prediction model is thus proposed. In the first phase, the GII data from the previous year are applied to predict the ranking of innovation capability for high-income countries. In the second phase, more years of historical data are adopted to predict the innovation ranking for other countries. The global innovation rankings for all countries and economies are thus obtained. Experiments have proved that the model requires only a few indicators to get accurate results. The model provides real-time decision support for decision-makers to formulate innovative development policies.

A millimeter-wave (mm-Wave) dual circularly polarized (CP) antenna in gap waveguide (GWG) technology with high port isolation is proposed in this paper. It is consisted of a simplified orthomode transducer (OMT) and an improved multi-section hexagonal waveguide CP horn antenna. The OMT is composed of two metal layers without the traditional septum or iris, which makes the structure simpler. The CP horn antenna can be easily integrated with the OMT without mode conversion. The principle analysis as well as the simulated and measured results of the proposed antenna are given in this paper. The simulated and measured results agree very well with each other. The port isolation of more than 27 dB over bandwidth of 26.5?31 GHz (|S₁₁|< ?15 dB) is achieved with relative bandwidth of 15.7%. The axial ratio (AR) lower than 2.5 dB for both left-hand and right-hand CP (LHCP and RHCP) are achieved over the bandwidth. The proposed antenna is a candidate for mm-Wave satellite communications or beyond fifth-generation (5G) communications applications.

We propose a deep-learning-assisted strapdown inertial navigation system (SINS)/refraction celestial navigation system (RCNS) integrated navigation method to control the adverse effects of atmospheric density errors on the accuracy of stellar refraction navigation and enhance the reliability of SINS/RCNS integrated navigation for aerospace vehicles. This method utilizes satellite navigation data and a long short-term memory network to establish a mapping relationship between the navigation moments, refraction angles, and the apparent height errors. Using deep learning algorithm to address complex time-series prediction problems, thereby compensates the impact of atmospheric density deviations on star sensor measurements. Simulation experiments of vehicle navigation in scenarios with atmospheric density errors are conducted using this method. The results show that the deep learning scheme can effectively resist the adverse effects of atmospheric density errors on navigation, demonstrating strong reliability.

Lyapunov-based model predictive control (LMPC) is an effective approach for trajectory tracking because of its well-guaranteed and easy-to-implement stability. However, traditional LMPC utilizes pre-designed auxiliary controllers to estimate the domain of attraction (DOA) and construct stability constraints, which inevitably reduces its stable domain and degrades tracking performance. For this problem, this paper proposes a relaxed LMPC (RLMPC) which is designed independently of auxiliary controllers. The control Lyapunov function (CLF) is firstly introduced to decouple the DOA and auxiliary control, alleviating the conservatism in traditional LMPC. Subsequently, a multi-resolution sampling-based search algorithm is developed to estimate the DOA, where the state space is partitioned into hyper-rectangles. A verification condition is derived to extend the verification validity of sampling points to all states within hyper-rectangles, thereby reducing DOA estimation error. Based on the auxiliary-controller-independent DOA (ACI-DOA) and CLF, stability constraints are formulated to ensure stability for RLMPC, while relaxing the stable domain of RLMPC to the entire ACI-DOA. Furthermore, a convergence rate adaptive adjustment technology is developed to enhance the convergence rate while balancing it with control effort. Through numerical simulations involving asteroid orbiting missions, the proposed method is found to significantly expand the stable domain and improve tracking performance.

Resolving conflict and achieving consensus among social groups with diverse opinions becomes a critical issue in today’s extensively connected society. Despite the ubiquitous heterogeneity of connection or contact patterns, the study of how topological characteristics of network structure affect opinion convergence is still insufficient. Based on Deffuant and colleagues’ bounded confidence model and the transformable network structure between random network and typical complex network types, including small-world network and scale-free network, we analyze the critical factors affecting continuous opinion convergence. We find that the network density plays a crucial role in the aggregated process of opinions in the social group, followed by the modularized level and the average shortest path length of the social network. However, the structural features have little impact on the consensus phase transition threshold. The further simulation experiments under real networks can be well understood based on the interplay of these three main factors. These findings confirm the paramount importance of creating a high-frequency and widely communicated atmosphere to mitigate conflict and efficiently reach consensus.

This paper investigates the six degree-of-freedom (6DOF) relative kinodynamic motion planning problem for spacecraft close approach operations, wherein a controlled chaser spacecraft is required to approach a noncooperative space target at a close range under both dynamic constraints and motion constraints. An enhanced version of the bidirectional rapidly-exploring random tree* (BiRRT*) algorithm based on flight zoning (FZ-BiRRT*) is proposed to generate safe, feasible, and near-optimal relative motion trajectories. In the proposed algorithm, the space surrounding the space target is zoned in a spherical coordinate system based on the collision probability so that specific designs can be made for different phases of the approaching. Subsequently, based on the flight zone, dynamic constraints, and experiential knowledge, a series of modifications are made to the classic BiRRT* algorithm, and a postprocessing step is designed to accelerate convergence and promote search efficiency. Furthermore, a general regression neural network is introduced to fit a smooth and applicable final motion trajectory. Finally, the feasibility of the generated motion trajectory and the superiority of the proposed algorithm is demonstrated by means of numerical simulations

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.

To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios, this paper proposes a multidimensional intention fusion-based inference method. Firstly, the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’ theorem. Secondly, the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions, and the corresponding likelihood probability is obtained by quantifying the cost of different intentions, which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving no-fly zones. Finally, a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints. The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods.

Agile imaging satellites play important roles in gathering Earth surface information due to their expansive field-of-view, high-resolution observations, and flexibility in observation timing. Genetic algorithm (GA) has been one of the most commonly utilized meta-heuristic algorithms for solving multiple agile satellites (MAS) scheduling problems due to its stable and good performance. Nonetheless, the iterative convergence speed of GA is slow owing to the blindness of the preliminary search. To address this issue, this paper proposes an efficient meta-heuristic MAS scheduling method integrated with a neural network (NN). In the proposed method, an NN is applied to learn the historical scheduling experience and generate effective initial solutions, thereby improving the convergence speed of GA for MAS scheduling. Experimental results demonstrate that the proposed NN-guided GA (NN-GA) accelerates the convergence of GA by approximately 60% and exhibits higher efficiency for MAS scheduling compared to existing state-of-the-art methods.

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.

The dramatic rise in vehicle numbers and the development of autonomous driving technology have underscored the importance of accurate environment perception to prevent traffic accidents. Due to its distinctive advantages, millimeter-wave radar has emerged as the dominant type of vehicle-mounted sensor. However, frequency band for automotive radar is restricted to 76−81 GHz, resulting in insufficient spectrum resources because of the high-resolution requirement. The rapid increase of radars in the actual traffic environment will inevitably lead to mutual interference between radars, posing a potential threat to public transportation. To address this problem, this paper investigates the mutual interference of automotive frequency modulated continuous wave (FMCW) radars, and mitigates the mutual interference utilizing a low-rank tensor model. By stacking the short-time Fourier transform (STFT) matrix of all chirps in a single channel as tensors, a tensor robust principal component analysis (TRPCA) algorithm is employed to separate the target signal and the interference. Mutual interference of multiple input multiple output (MIMO) radar could be suppressed by applying TRPCA in all channels. Both simulations and numerical experiments demonstrate that the proposed method achieves higher signal to interference plus noise ratio (SINR) and lower root mean square error (RMSE) compared with other methods, thereby proving its superior interference suppression capability in different practical scenarios.

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.

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.

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.

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.

Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.

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.

Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.

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.

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.

The emergence of laser technology has led to the gradual integration of laser weapon system (LaWS) into military scene, particularly in the field of anti-unmanned aerial vehicle (UAV), showcasing significant potential. However, A current limitation lies in the absence of a comprehensive quantitative approach to assess the capabilities of LaWS. To address this issue, a damage effectiveness characterization model for LaWS is established, taking into account the properties of laser transmission through the atmosphere and the thermal damage effects. By employing this model, key parameters pertaining to the effectiveness of laser damage are determined. The impact of various spatial positions and atmospheric conditions on the damage effectiveness of LaWS have been examined, employing simulation experiments with diverse parameters. The conclusions indicate that the damage effectiveness of LaWS is contingent upon the spatial position of the target, resulting in a diminished effectiveness to damage on distant, low-altitude targets. Additionally, the damage effectiveness of LaWS is heavily reliant on the atmospheric condition, particularly in complex settings such as midday and low visibility conditions, where the damage effectiveness is substantially reduced. This paper provides an accurate and effective calculation method for the rapid decision-making of the operators.

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.

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.

To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition. This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat. This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment. Simulation results demonstrate that, compared to classical intention recognition models, the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.

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.

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.

Lightweight convolutional neural networks (CNNs) have simple structures but struggle to comprehensively and accurately extract important semantic information from images. While attention mechanisms can enhance CNNs by learning distinctive representations, most existing spatial and hybrid attention methods focus on local regions with extensive parameters, making them unsuitable for lightweight CNNs. In this paper, we propose a self-attention mechanism tailored for lightweight networks, namely the brief self-attention module (BSAM). BSAM consists of the brief spatial attention (BSA) and advanced channel attention blocks. Unlike conventional self-attention methods with many parameters, our BSA block improves the performance of lightweight networks by effectively learning global semantic representations. Moreover, BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training, maintaining the network’s lightweight and mobile characteristics. We validate the effectiveness of the proposed method on image classification tasks using the Food-101, Caltech-256, and Mini-ImageNet datasets.

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.