Future unmanned battles desperately require intelligent combat policies, and multi-agent reinforcement learning offers a promising solution. However, due to the complexity of combat operations and large size of the combat group, this task suffers from credit assignment problem more than other reinforcement learning tasks. This study uses reward shaping to relieve the credit assignment problem and improve policy training for the new generation of large-scale unmanned combat operations. We first prove that multiple reward shaping functions would not change the Nash Equilibrium in stochastic games, providing theoretical support for their use. According to the characteristics of combat operations, we propose tactical reward shaping (TRS) that comprises maneuver shaping advice and threat assessment-based attack shaping advice. Then, we investigate the effects of different types and combinations of shaping advice on combat policies through experiments. The results show that TRS improves both the efficiency and attack accuracy of combat policies, with the combination of maneuver reward shaping advice and ally-focused attack shaping advice achieving the best performance compared with that of the baseline strategy.

To tackle the challenges of intractable parameter tuning, significant computational expenditure and imprecise model-driven sparse-based direction of arrival (DOA) estimation with array error (AE), this paper proposes a deep unfolded amplitude-phase error self-calibration network. Firstly, a sparse-based DOA model with an array convex error restriction is established, which gets resolved via an alternating iterative minimization (AIM) algorithm. The algorithm is then unrolled to a deep network known as AE-AIM Network (AE-AIM-Net), where all parameters are optimized through multi-task learning using the constructed complete dataset. The results of the simulation and theoretical analysis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery methods. Furthermore, it maintains excellent estimation performance even in the presence of array magnitude-phase errors.

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.

Discrete event system (DES) models promote system engineering, including system design, verification, and assessment. The advancement in manufacturing technology has endowed us to fabricate complex industrial systems. Consequently, the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative. Moreover, industrial systems are no longer quiescent, thus the intelligent operations of the systems should be dynamically specified in the model. In this paper, the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model, and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model. In traditional modeling approaches, the change or addition of specifications always necessitates the complete resubmission of the system model, a resource-consuming and error-prone process. Compared with traditional approaches, our approach has three remarkable advantages: (i) an established Boolean semantic can be fitful for all kinds of systems; (ii) there is no need to resubmit the system model whenever there is a change or addition of the operations; (iii) multiple specifying tasks can be easily achieved by continuously adding a new semantic. Thus, this general modeling approach has wide potential for future complex and intelligent industrial systems.

Aiming at the characteristics of multi-stage and (extremely) small samples of the identification problem of key effectiveness indexes of weapon equipment system-of-systems (WESoS), a Bayesian intelligent identification and inference model for system effectiveness assessment indexes based on dynamic grey incidence is proposed. The method uses multilayer Bayesian techniques, makes full use of historical statistics and empirical information, and determines the Bayesian estimation of the incidence degree of indexes, which effectively solves the difficulties of small sample size of effectiveness indexes and difficulty in obtaining incidence rules between indexes. Secondly, The method quantifies the incidence relationship between evaluation indexes and combat effectiveness based on Bayesian posterior grey incidence, and then identifies key system effectiveness evaluation indexes. Finally, the proposed method is applied to a case of screening key effectiveness indexes of a missile defensive system, and the analysis results show that the proposed method can fuse multi-moment information and extract multi-stage key indexes, and has good data extraction capability in the case of small samples.

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.

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.

During high-speed flight, both thermal and mechanical loads can degrade the electrical performance of the antenna-radome system, which can subsequently affect the performance of the guidance system. This paper presents a method for evaluating the electrical performance of the radome when subjected to thermo-mechanical-electrical (TME) coupling. The method involves establishing a TME coupling model (TME-CM) based on the TME sharing mesh model (TME-SMM) generated by the tetrahedral mesh partitioning of the radome structure. The effects of dielectric temperature drift and structural deformation on the radome’s electrical performance are also considered. Firstly, the temperature field of the radome is obtained by transient thermal analysis while the deformation field of the radome is obtained by static analysis. Subsequently, the dielectric variation and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM. The three-dimensional (3D) ray tracing method with the aperture integration technique is used to calculate the radome’s electrical performance. A representative example is provided to illustrate the superiority and necessity of the proposed method. This is achieved by calculating and analyzing the changes in the radome’s electrical performance over time during high-speed flight.

This paper addresses the time-varying formation-containment (FC) problem for nonholonomic multi-agent systems with a desired trajectory constraint, where only the leaders can acquire information about the desired trajectory. Input the fixed time-varying formation template to the leader and start executing, this process also needs to track the desired trajectory, and the follower needs to converge to the convex hull that the leader crosses. Firstly, the dynamic models of nonholonomic systems are linearized to second-order dynamics. Then, based on the desired trajectory and formation template, the FC control protocols are proposed. Sufficient conditions to achieve FC are introduced and an algorithm is proposed to resolve the control parameters by solving an algebraic Riccati equation. The system is demonstrated to achieve FC, with the average position and velocity of the leaders converging asymptotically to the desired trajectory. Finally, the theoretical achievements are verified in simulations by a multi-agent system composed of virtual human individuals.

A framework that integrates planning, monitoring and replanning techniques is proposed. It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution. The framework consists of three parts: the hierarchical task network (HTN) planner based on Monte Carlo tree search (MCTS), hybrid plan monitoring based on forward and backward and norm-based replanning method selection. The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration. Based on specific objectives, it can identify the best solution to the current problem. The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action, thus trigger the replanning. The norm-based replanning selection method can measure the difference between the expected state and the actual state, and then select the best replanning algorithm. The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

Cascading failures in infrastructure networks have serious impacts on network function. The limited capacity of network nodes provides a necessary condition for cascade failure. However, the network capacity cannot be infinite in the real network system. Therefore, how to reasonably allocate the limited capacity resources is of great significance. In this article, we put forward a capacity allocation strategy based on community structure against cascading failure. Experimental results indicate that the proposed method can reduce the scale of cascade failures with higher capacity utilization compared with Motter-Lai (ML) model. The advantage of our method is more obvious in scale-free network. Furthermore, the experiment shows that the cascade effect is more obvious when the vertex load is randomly varying. It is known to all that the growth of network capacity can make the network more resistant to destruction, but in this paper it is found that the contribution rate of unit capacity rises first and then decreases with the growth of network capacity cost.

Separation and recognition of radar signals is the key function of modern radar reconnaissance, which is of great significance for electronic countermeasures and anti-countermeasures. In order to improve the ability of separating mixed signals in complex electromagnetic environment, a blind source separation algorithm based on degree of cyclostationarity (DCS) criterion is constructed in this paper. Firstly, the DCS criterion is constructed by using the cyclic spectrum theory. Then the algorithm flow of blind source separation is designed based on DCS criterion. At the same time, Givens matrix is constructed to make the blind source separation algorithm suitable for multiple signals with different cyclostationary frequencies. The feasibility of this method is further proved. The theoretical and simulation results show that the algorithm can effectively separate and recognize common multi-radar signals.

With the continuous development of network functions virtualization (NFV) and software-defined networking (SDN) technologies and the explosive growth of network traffic, the requirement for computing resources in the network has risen sharply. Due to the high cost of edge computing resources, coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge. In this paper, we focus on optimizing the placement of network services in cloud-edge environments to maximize the efficiency. It is first proved that, in cloud-edge environments, placing one service function chain (SFC) integrally in the cloud or at the edge can improve the utilization efficiency of edge resources. Then a virtual network function (VNF) performance-resource (P-R) function is proposed to represent the relationship between the VNF instance computing performance and the allocated computing resource. To select the SFCs that are most suitable to deploy at the edge, a VNF placement and resource allocation model is built to configure each VNF with its particular P-R function. Moreover, a heuristic recursive algorithm is designed called the recursive algorithm for max edge throughput (RMET) to solve the model. Through simulations on two scenarios, it is verified that RMET can improve the utilization efficiency of edge computing resources.

The parametric scattering center model of radar target has the advantages of simplicity, sparsity and mechanism relevant, making it widely applied in fields such as radar data compression and rapid generation, radar imaging, feature extraction and recognition. This paper summarizes and analyzes the research situation, development trend, and difficult problems on scattering center (SC) parametric modeling from three aspects: parametric representation, determination method of model parameters, and application.

The rapid evolution of unmanned aerial vehicle (UAV) technology and autonomous capabilities has positioned UAV as promising last-mile delivery means. Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode. Spatiotemporal collaboration, along with energy consumption with payload and wind conditions play important roles in delivery route planning. This paper introduces the traveling salesman problem with time window and onboard UAV (TSP-TWOUAV) and emphasizes the consideration of real-world scenarios, focusing on time collaboration and energy consumption with wind and payload. To address this, a mixed integer linear programming (MILP) model is formulated to minimize the energy consumption costs of vehicle and UAV. Furthermore, an adaptive large neighborhood search (ALNS) algorithm is applied to identify high-quality solutions efficiently. The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.

Vibration-induced bias deviation, which is generated by intensity fluctuations and additional phase differences, is one of the vital errors for fiber optic gyroscopes (FOGs) operating in vibration environment and has severely restricted the applications of high-precision FOGs. The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters, which have very limited effects for high-precision FOGs maintaining performances under vibration. In this work, a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward. Particularly, the loop gain is extracted out by adding a gain-monitoring wave. By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship, the vibration-induced bias error is compensated without limiting the operating parameters or environments, like the applied modulation depth. The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to 0.014°/h during the random vibration with frequencies from 20 Hz to 2000 Hz. This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

A millimeter-wave (mmW) broadband dual circularly polarized (dual-CP) antenna with high port isolation is proposed in this paper. The dual-CP performance is realized based on the symmetrical septum circular polarizer based on the gap waveguide (GWG) technology. Two sets of symmetrical septum circular polarizers are used for common aperture combination, achieving the broadband dual-CP characteristics. Taking advantage of GWG structure without good electrical contact, the antenna can also be fabricated and assembled easily in the mmW band. The principle analysis of the antenna is given, and the antenna is simulated and fabricated. The measured results show that the bandwidth for S₁₁ lower than ?10.7 dB and the axial ratio (AR) lower than 2.90 dB in 75?110 GHz, with realative bandwidth of 38%. Over the frequency band, the gain is higher than 9.16 dBic, and the dual-CP port isolation is greater than 32 dB. The proposed antenna with dual-CP and highly isolated in a wide bandwidth range has broad application prospects in the field of mmW communication.

Ground penetrating radar (GPR), as a fast, efficient, and non-destructive detection device, holds great potential for the detection of shallow subsurface environments, such as urban road subsurface monitoring. However, the interpretation of GPR echo images often relies on manual recognition by experienced engineers. In order to address the automatic interpretation of cavity targets in GPR echo images, a recognition-algorithm based on Gaussian mixed model-hidden Markov model (GMM-HMM) is proposed, which can recognize three dimensional (3D) underground voids automatically. First, energy detection on the echo images is performed, whereby the data is pre-processed and pre-filtered. Then, edge histogram descriptor (EHD), histogram of oriented gradient (HOG), and Log-Gabor filters are used to extract features from the images. The traditional method can only be applied to 2D images and pre-processing is required for C-scan images. Finally, the aggregated features are fed into the GMM-HMM for classification and compared with two other methods, long short-term memory (LSTM) and gate recurrent unit (GRU). By testing on a simulated dataset, an accuracy rate of 90% is obtained, demonstrating the effectiveness and efficiency of our proposed method.

To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model. Therefore, the modeling idea of the mixture of experts (MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis (PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.

Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks (MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-to-end delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a low-Earth-orbit satellite communication network (LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.

When the maneuverability of a pursuer is not significantly higher than that of an evader, it will be difficult to intercept the evader with only one pursuer. Therefore, this article adopts a two-to-one differential game strategy, the game of kind is generally considered to be angle-optimized, which allows unlimited turns, but these practices do not take into account the effect of acceleration, which does not correspond to the actual situation, thus, based on the angle-optimized, the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration. A two-to-one differential game problem is proposed in the three-dimensional space, and an improved multi-objective grey wolf optimization (IMOGWO) algorithm is proposed to solve the optimal game point of this problem. With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space, a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game. Then the optimal game point is solved by using the IMOGWO algorithm. It is proved based on Markov chains that with the IMOGWO, the Pareto solution set is the solution of the differential game. Finally, it is verified through simulations that the pursuers can capture the escapee, and via comparative experiments, it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.

Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process (AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.

Failure mode and effect analysis (FMEA) is a preventative risk evaluation method used to evaluate and eliminate failure modes within a system. However, the traditional FMEA method exhibits many deficiencies that pose challenges in practical applications. To improve the conventional FMEA, many modified FMEA models have been suggested. However, the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes. In this research, we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clustering algorithm for the assessment and clustering of failure modes. Firstly, we employ the interval 2-tuple linguistic variables (I2TLVs) to express the uncertain risk evaluations provided by FMEA experts. Then, a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus. Next, failure modes are categorized into several risk clusters using a density peak clustering algorithm. Finally, the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems. The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs; the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching; and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.

This paper investigates the selective maintenance of systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.

The process of ground vehicle dynamic gravimetry is inevitably affected by the carrier’s maneuvering acceleration, which makes the result contain a large amount of dynamic error. In this paper, we propose a dynamic error suppression method of gravimetry based on the high-precision acquisition of external velocity for compensating the horizontal error of the inertial platform. On the basis of platform gravity measurement, firstly, the dynamic performance of the system is enhanced by optimizing the horizontal damping network of the inertial platform and selecting its parameter. Secondly, an improved federal Kalman filtering algorithm and a fault diagnosis method are designed using strapdown inertial navigation system (SINS), odometer (OD), and laser Doppler velocimeter (LDV). Simulation validates that these methods can improve the accuracy and robustness of the external velocity acquisition. Three survey lines are selected in Tianjin, China, for the gravimetry experiments with different maneuvering levels, and the results demonstrate that after dynamic error suppression, the internal coincidence accuracies of smooth and uniform operation, obvious acceleration and deceleration operation, and high-dynamic operation are improved by 70.2%, 73.6%, and 77.9% to reach 0.81 mGal, 1.30 mGal, and 1.94 mGal, respectively, and the external coincidence accuracies during smooth and uniform operation are improved by 48.6% up to 1.66 mGal. It is shown that the proposed method can effectively suppress the dynamic error, and that the accuracy improvement increases with carrier maneuverability. However, the amount of residual error that can not be entirely eliminated increases as well, so the ground vehicle dynamic gravimetry should be maintained in the carrier for smooth and uniform operation.

Based on the variation of discrete surface, a new grey relational analysis model, called the grey variation relational analysis (GVRA) model, is proposed in this paper. Meanwhile, the proposed model avoids the inconsistent results caused by different construction of discrete surface of panel data or the change in the order of indicators or objects in existing grey relational analysis models. Firstly, the submatrix of the sample matrix is given according to the permutation and combination theory. Secondly, the amplitude of the submatrix is calculated and the variation of discrete surface is obtained. Then, a grey relational coefficient is presented by variation difference, and the GVRA model is established. Furthermore, the properties of the proposed model, such as normality, symmetry, reflexivity, translation invariant, and number multiplication invariant, are also verified. Finally, the proposed model is used to identify the driving factors of haze in the cities along the Yellow River in Shandong Province, China. The result reveals that the proposed model can effectively measure the relationship between panel data.

Separated transmit and receive antennas are employed to improve transmit-receive isolation in conventional short-range radars, which greatly increases the antenna size and misaligns of the transmit/receive radiation patterns. In this paper, a dual circularly polarized (CP) monostatic simultaneous transmit and receive (MSTAR) antenna with enhanced isolation is proposed to alleviate the problem. The proposed antenna consists of one sequentially rotating array (SRA), two beamforming networks (BFN), and a combined decoupling structure. The SRA is shared by the transmit and receive to reduce the size of the antenna and to obtain a consistent transmit and receive pattern. The BFN achieve right-hand CP for transmit and left-hand CP for receive. By exploring the combined decoupling structure of uniplanar compact electromagnetic band gap (UC-EBG) and ring-shaped defected ground structure (RS-DGS), good transmit-receive isolation is achieved. The proposed antenna prototype is fabricated and experimentally characterized. The simulated and measured results show good agreement. The demonstrate transmit/receive isolation is height than 33 dB, voltage standing wave ratio is lower than 2, axial ratio is lower than 3 dB, and consistent radiation for both transmit and receive is within 4.25?4.35 GHz.

In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios, the limitations of existing research, including real-time calculation, accuracy efficiency trade-off, and the absence of the three-dimensional attack area model, restrict their practical applications. To address these issues, an improved backtracking algorithm is proposed to improve calculation efficiency. A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm. Furthermore, the age-layered population structure genetic programming (ALPS-GP ) algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area, considering real-time requirements. The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm. The study reveals a remarkable combination of high accuracy and efficient real-time computation, with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10^?4 s, thus meeting the requirements of real-time combat scenarios.

To meet the requirements of modern air combat, an integrated fire/flight control (IFFC) system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden. Considering the complex dynamic characteristics and the couplings of armed helicopters, an improved automatic attack system is constructed to integrate the fire control system with the flight control system into a unit. To obtain the optimal command signals, the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno (C-BFGS) algorithm combined with the trust region method. To address the uncertainties in the automatic attack system, the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties, and the dual solvable problem is analyzed by using the duality theory, conjugate function, and dual norm. Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accuracy.

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.

By deploying the ubiquitous and reliable coverage of low Earth orbit (LEO) satellite networks using optical inter satellite link (OISL), computation offloading services can be provided for any users without proximal servers, while the resource limitation of both computation and storage on satellites is the important factor affecting the maximum task completion time. In this paper, we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs, such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood. To satisfy the delay requirement of delay-sensitive task, we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline, and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites. Simulation results demonstrate the effectiveness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and flexibility. However, beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal, satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-performance reception. Firstly, this paper analyzes the key issues of burst communication for traffic signals in beam hopping systems, and then compares and studies typical carrier synchronization algorithms for burst signals. Secondly, combining the requirements of beam-hopping communication systems for efficient burst and low signal-to-noise ratio reception of downlink signals in forward links, a decoding assisted bidirectional variable parameter iterative carrier synchronization technique is proposed, which introduces the idea of iterative processing into carrier synchronization. Aiming at the technical characteristics of communication signal carrier synchronization, a new technical approach of bidirectional variable parameter iteration is adopted, breaking through the traditional understanding that loop structures cannot adapt to low signal-to-noise ratio burst demodulation. Finally, combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems, the research and performance simulation are conducted. The results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals, achieve fast synchronization of low signal-to-noise ratio burst signals, and have the unique advantage of flexible and adjustable parameters.

The acquisition, analysis, and prediction of the radar cross section (RCS) of a target have extremely important strategic significance in the military. However, the RCS values at all azimuths are hardly accessible for non-cooperative targets, due to the limitations of radar observation azimuth and detection resources. Despite their efforts to predict the azimuth-dimensional RCS value, traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS. To address this problem, an improved neural basis expansion analysis for interpretable time series forecasting (N-BEATS) network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately. Concretely, physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model. Besides, a superimposed seasonality module is involved to better capture high-frequency information, and augmenting the training set provides complementary information for learning predictions. Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.

A low-Earth-orbit (LEO) satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking. However, the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service. Moreover, the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas. To enhance the forwarding capability of satellite networks, we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall. Then, we propose a multi-region cooperative traffic scheduling algorithm. The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding, significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding. This algorithm can utilize all the global satellite resources and improve the utilization of network resources. We model the cooperative multi-region scheduling of large-scale LEO satellites. Based on the model, we build a system testbed using OMNET++ to compare the proposed method with existing techniques. The simulations show that our proposed method can reduce the packet loss probability by 30% and improve the resource utilization ratio by 3.69%.

The direction of ground-based interference reaching the satellite is generally very close to the spot beam of the satellite. The traditional array anti-jamming method may cause significant loss to the uplink signal while suppressing the interference. In this paper, an aperiodic multistage array is used, and a sub-array aperiodic distribution optimization scheme based on parallel differential evolution is proposed, which effectively improves the beam resolution and suppresses the grating lobe. On this basis, a two-stage signal processing method is used to suppress interference. Finally, the comprehensive performance of the proposed scheme is evaluated and verified.