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.

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.

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.

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.

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%.

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%.

A dynamic multi-beam resource allocation algorithm for large low Earth orbit (LEO) constellation based on on-board distributed computing is proposed in this paper. The allocation is a combinatorial optimization process under a series of complex constraints, which is important for enhancing the matching between resources and requirements. A complex algorithm is not available because that the LEO on-board resources is limited. The proposed genetic algorithm (GA) based on two-dimensional individual model and uncorrelated single paternal inheritance method is designed to support distributed computation to enhance the feasibility of on-board application. A distributed system composed of eight embedded devices is built to verify the algorithm. A typical scenario is built in the system to evaluate the resource allocation process, algorithm mathematical model, trigger strategy, and distributed computation architecture. According to the simulation and measurement results, the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91% in a typical scene. The response time is decreased by 40% compared with the conditional GA.

Imaging detection is an important means to obtain target information. The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target. In practical applications, due to the mixed scenario, it is difficult to meet the needs of target recognition. Compared with intensity detection, the method of polarization detection can effectively enhance the accuracy of ground object target recognition (such as the camouflage target). In this paper, the reflection mechanism of the target surface is studied from the microscopic point of view, and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters. The polarization characteristic test experiment is carried out, and the target surface parameters are retrieved using the experimental data. The results show that the degree of polarization (DOP) is closely related to the detection zenith angle and azimuth angle. The (DOP) of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection. Different materials have different polarization characteristics. By comparing their DOP, target classification can be achieved.

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.

Low Earth orbit (LEO) satellite networks exhibit distinct characteristics, e.g., limited resources of individual satellite nodes and dynamic network topology, which have brought many challenges for routing algorithms. To satisfy quality of service (QoS) requirements of various users, it is critical to research efficient routing strategies to fully utilize satellite resources. This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks, which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources. An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm. Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.

To analyze the influence of time synchronization error, phase synchronization error, frequency synchronization error, internal delay of the transceiver system, and range error and angle error between the unit radars on the target detection performance, firstly, a spatial detection model of distributed high-frequency surface wave radar (distributed-HFSWR) is established in this paper. In this model, a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio (SNR), and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error. The direct wave component is extracted from the spectrum, the range estimation error and Doppler estimation error are reduced by the method of curve fitting, and the fitting accuracy of the parameters is improved. Then, the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed. The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given. Finally, the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting, the experimental data is compensated to correct the shift of the target, and finally the correct target parameter information is obtained. Simulations and experimental results demonstrate the superiority and correctness of the proposed method, theoretical derivation and detection model proposed in this paper.

In this paper, a feature selection method for determining input parameters in antenna modeling is proposed. In antenna modeling, the input feature of artificial neural network (ANN) is geometric parameters. The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic (EM) response. Maximal information coefficient (MIC), an exploratory data mining tool, is introduced to evaluate both linear and nonlinear correlations. The EM response range is utilized to evaluate the sensitivity. The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive. Only the parameter which is highly correlative and sensitive is selected as the input of ANN, and the sampling space of the model is highly reduced. The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method. The number of input parameters decreases from 8 to 4. The testing errors of |S₁₁| and axis ratio are reduced by 8.74% and 8.95%, respectively, compared with the ANN with no feature selection.

Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.

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.

There is a growing body of research on the swarm unmanned aerial vehicle (UAV) in recent years, which has the characteristics of small, low speed, and low height as radar target. To confront the swarm UAV, the design of anti-UAV radar system based on multiple input multiple output (MIMO) is put forward, which can elevate the performance of resolution, angle accuracy, high data rate, and tracking flexibility for swarm UAV detection. Target resolution and detection are the core problem in detecting the swarm UAV. The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar. Since MIMO radar has better performance in resolution, swarm UAV detection still has difficulty in target detection. This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect. Subsequently, signal processing and data processing based on the detection fusion algorithm above are designed, forming a high resolution detection loop. Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

Beyond-visual-range (BVR) air combat threat assessment has attracted wide attention as the support of situation awareness and autonomous decision-making. However, the traditional threat assessment method is flawed in its failure to consider the intention and event of the target, resulting in inaccurate assessment results. In view of this, an integrated threat assessment method is proposed to address the existing problems, such as overly subjective determination of index weight and imbalance of situation. The process and characteristics of BVR air combat are analyzed to establish a threat assessment model in terms of target intention, event, situation, and capability. On this basis, a distributed weight-solving algorithm is proposed to determine index and attribute weight respectively. Then, variable weight and game theory are introduced to effectively deal with the situation imbalance and achieve the combination of subjective and objective. The performance of the model and algorithm is evaluated through multiple simulation experiments. The assessment results demonstrate the accuracy of the proposed method in BVR air combat, indicating its potential practical significance in real air combat scenarios.

With the rapid development of low-orbit satellite communication networks both domestically and internationally, space-terrestrial integrated networks will become the future development trend. For space and terrestrial networks with limited resources, the utilization efficiency of the entire space-terrestrial integrated networks resources can be affected by the core network indirectly. In order to improve the response efficiency of core networks expansion construction, early warning of the core network elements capacity is necessary. Based on the integrated architecture of space and terrestrial network, multidimensional factors are considered in this paper, including the number of terminals, login users, and the rules of users’ migration during holidays. Using artifical intelligence (AI) technologies, the registered users of the access and mobility management function (AMF), authorization users of the unified data management (UDM), protocol data unit (PDU) sessions of session management function (SMF) are predicted in combination with the number of login users, the number of terminals. Therefore, the core network elements capacity can be predicted in advance. The proposed method is proven to be effective based on the data from real network.

With the rapid development of cloud manufacturing technology and the new generation of artificial intelligence technology, the new cloud manufacturing system (NCMS) built on the connotation of cloud manufacturing 3.0 presents a new business model of “Internet of everything, intelligent leading, data driving, shared services, cross-border integration, and universal innovation”. The network boundaries are becoming increasingly blurred, NCMS is facing security risks such as equipment unauthorized use, account theft, static and extensive access control policies, unauthorized access, supply chain attacks, sensitive data leaks, and industrial control vulnerability attacks. Traditional security architectures mainly use information security technology, which cannot meet the active security protection requirements of NCMS. In order to solve the above problems, this paper proposes an integrated cloud-edge-terminal security system architecture of NCMS. It adopts the zero trust concept and effectively integrates multiple security capabilities such as network, equipment, cloud computing environment, application, identity, and data. It adopts a new access control mode of “continuous verification + dynamic authorization”, classified access control mechanisms such as attribute-based access control, role-based access control, policy-based access control, and a new data security protection system based on blockchain, achieving “trustworthy subject identity, controllable access behavior, and effective protection of subject and object resources”. This architecture provides an active security protection method for NCMS in the digital transformation of large enterprises, and can effectively enhance network security protection capabilities and cope with increasingly severe network security situations.

In challenging situations, such as low illumination, rain, and background clutter, the stability of the thermal infrared (TIR) spectrum can help red, green, blue (RGB) visible spectrum to improve tracking performance. However, the high-level image information and the modality-specific features have not been sufficiently studied. The proposed correlation filter uses the fused saliency content map to improve filter training and extracts different features of modalities. The fused content map is introduced into the spatial regularization term of correlation filter to highlight the training samples in the content region. Furthermore, the fused content map can avoid the incompleteness of the content region caused by challenging situations. Additionally, different features are extracted according to the modality characteristics and are fused by the designed response-level fusion strategy. The alternating direction method of multipliers (ADMM) algorithm is used to solve the tracker training efficiently. Experiments on the large-scale benchmark datasets show the effectiveness of the proposed tracker compared to the state-of-the-art traditional trackers and the deep learning based trackers.

To reduce the negative impact of the power amplifier (PA) nonlinear distortion caused by the orthogonal frequency division multiplexing (OFDM) waveform with high peak-to-average power ratio (PAPR) in integrated radar and communication (RadCom) systems is studied, the channel estimation in passive sensing scenarios. Adaptive channel estimation methods are proposed based on different pilot patterns, considering nonlinear distortion and channel sparsity. The proposed methods achieve sparse channel results by manipulating the least squares (LS) frequency-domain channel estimation results to preserve the most significant taps. The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion. Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns. In addition, the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

In this paper, the reactive splitter network and metasurface are proposed to radiate the wide-beam isolated element pattern and suppress mutual coupling (MC) of the low-profile phased array with the triangular lattice, respectively. Thus, broadband wide-angle impedance matching (WAIM) is implemented to promote two-dimensional (2D) wide scanning. For the isolated element, to radiate the wide-beam patterns approximating to the cosine form, two identical slots backed on one substrate integrated cavity are excited by the feeding network consisting of a reactive splitter and two striplines connected with splitter output paths. For adjacent elements staggered with each other, with the metasurface superstrate, the even-mode coupling voltages on the reactive splitter are cancelled out, yielding reduced MC. With the suppression of MC and the compensation of isolated element patterns, WAIM is realized to achieve 2D wide-angle beam steering up to ± 65° in E-plane, ± 45° in H-plane and ± 60° in D-plane from 4.9 GHz to 5.85 GHz.

A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper. Considering that the actual mission environment information may be unknown, the UAV swarm needs to detect the environment first and then attack the detected targets. The heterogeneity of UAVs, multiple types of tasks, and the dynamic nature of task environment lead to uneven load and time sequence problems. This paper proposes an improved contract net protocol (CNP) based task allocation scheme, which effectively balances the load of UAVs and improves the task efficiency. Firstly, two types of task models are established, including regional reconnaissance tasks and target attack tasks. Secondly, for regional reconnaissance tasks, an improved CNP algorithm using the uncertain contract is developed. Through uncertain contracts, the area size of the regional reconnaissance task is determined adaptively after this task assignment, which can improve reconnaissance efficiency and resource utilization. Thirdly, for target attack tasks, an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation. Finally, the effectiveness and advantages of the improved method are verified through comparison simulations.

Final velocity and impact angle are critical to missile guidance. Computationally efficient guidance law with comprehensive consideration of the two performance merits is challenging yet remains less addressed. Therefore, this paper seeks to solve a type of optimal control problem that maximizes final velocity subject to equality point constraint of impact angle constraint. It is proved that the crude problem of maximizing final velocity is equivalent to minimizing a quadratic-form cost of curvature. The closed-form guidance law is henceforth derived using optimal control theory. The derived analytical guidance law coincides with the widely-used optimal guidance law with impact angle constraint (OGL-IAC) with a set of navigation parameters of two and six. On this basis, the optimal emission angle is determined to further increase the final velocity. The derived optimal value depends solely on the initial line-of-sight angle and impact angle constraint, and thus practical for real-world applications. The proposed guidance law is validated by numerical simulation. The results show that the OGL-IAC is superior to the benchmark guidance laws both in terms of final velocity and missing distance.

In consideration of the field-of-view (FOV) angle constraint, this study focuses on the guidance problem with impact time control. A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle constraint. On basis of the framework of the proportional navigation guidance, an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm, in which the reward functions are developed to decrease the time-to-go error and improve the terminal guidance accuracy. The numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics. Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel, execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

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.

Architecture framework has become an effective method recently to describe the system of systems (SoS) architecture, such as the United States (US) Department of Defense Architecture Framework Version 2.0 (DoDAF2.0). As a viewpoint in DoDAF2.0, the operational viewpoint (OV) describes operational activities, nodes, and resource flows. The OV models are important for SoS architecture development. However, as the SoS complexity increases, constructing OV models with traditional methods exposes shortcomings, such as inefficient data collection and low modeling standards. Therefore, we propose an intelligent modeling method for five OV models, including operational resource flow OV-2, organizational relationships OV-4, operational activity hierarchy OV-5a, operational activities model OV-5b, and operational activity sequences OV-6c. The main idea of the method is to extract OV architecture data from text and generate interoperable OV models. First, we construct the OV meta model based on the DoDAF2.0 meta model (DM2). Second, OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field (BiLSTM-CRF) model. And OV architecture relationships are collected with relationship extraction rules. Finally, we define the generation rules for OV models and develop an OV modeling tool. We use unmanned surface vehicles (USV) swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.

The application scope of the forward scatter radar (FSR) based on the Global Navigation Satellite System (GNSS) can be expanded by improving the detection capability. Firstly, the forward-scatter signal model when the target crosses the baseline is constructed. Then, the detection method of the forward-scatter signal based on the Rényi entropy of time-frequency distribution is proposed and the detection performance with different time-frequency distributions is compared. Simulation results show that the method based on the smooth pseudo Wigner-Ville distribution (SPWVD) can achieve the best performance. Next, combined with the geometry of FSR, the influence on detection performance of the relative distance between the target and the baseline is analyzed. Finally, the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate (CFAR) detection.

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion, but the expert knowledge is not fully utilized, a heterogeneous information fusion recognition method based on belief rule structure is proposed. By defining the continuous probabilistic hesitation fuzzy linguistic term sets (CPHFLTS) and establishing CPHFLTS distance measure, the belief rule base of the relationship between feature space and category space is constructed through information integration, and the evidence reasoning of the input samples is carried out. The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition. Compared with the other methods, the proposed method has a higher correct recognition rate under different noise levels.

Low-frequency signals have been proven valuable in the fields of target detection and geological exploration. Nevertheless, the practical implementation of these signals is hindered by large antenna diameters, limiting their potential applications. Therefore, it is imperative to study the creation of low-frequency signals using antennas with suitable dimensions. In contrast to conventional mechanical antenna techniques, our study generates low-frequency signals in the spatial domain utilizing the principle of the Doppler effect. We also defines the antenna array architecture, the timing sequency, and the radiating element signal waveform, and provides experimental prototypes including 8/64 antennas based on earlier research. In the conducted experiments, 121 MHz, 40 MHz, and 10 kHz composite signals are generated by 156 MHz radiating element signals. The composite signal spectrum matches the simulations, proving our low-frequency signal generating method works. This holds significant implications for research on generating low-frequency signals with small-sized antennas.

Model-based system-of-systems (SOS) engineering (MBSoSE) is becoming a promising solution for the design of SoS with increasing complexity. However, bridging the models from the design phase to the simulation phase poses significant challenges and requires an integrated approach. In this study, a unified requirement modeling approach is proposed based on unified architecture framework (UAF). Theoretical models are proposed which compose formalized descriptions from both top-down and bottom-up perspectives. Based on the description, the UAF profile is proposed to represent the SoS mission and constituent systems (CS) goal. Moreover, the agent-based simulation information is also described based on the overview, design concepts, and details (ODD) protocol as the complement part of the SoS profile, which can be transformed into different simulation platforms based on the eXtensible markup language (XML) technology and model-to-text method. In this way, the design of the SoS is simulated automatically in the early design stage. Finally, the method is implemented and an example is given to illustrate the whole process.

Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.

This paper considers an intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) system. To maximize the average achievable rate (AAR) under outdated channel state information (CSI), we propose a twin-timescale passive beamforming (PBF) and power allocation protocol which can reduce the IRS configuration and training overhead. Specifically, the short-timescale power allocation is designed with the outdated precoder and fixed PBF. A new particle swarm optimization (PSO)-based long-timescale PBF optimization is proposed, where mini-batch channel samples are utilized to update the fitness function. Finally, simulation results demonstrate the effectiveness of the proposed method.

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets, a guidance law with temporal consistency constraint based on the super-twisting observer is proposed. Firstly, the relative motion equations between multiple missiles and targets are established, and the topological model among multiple agents is considered. Secondly, based on the temporal consistency constraint, a cooperative guidance law for simultaneous arrival with finite-time convergence is derived. Finally, the unknown target maneuvering is regarded as bounded interference. Based on the second-order sliding mode theory, a super-twisting sliding mode observer is devised to observe and track the bounded interference, and the stability of the observer is proved. Compared with the existing research, this approach only needs to obtain the sliding mode variable which simplifies the design process. The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect. It ensures successful cooperative attacks, even when confronted with strong maneuvering targets.