To solve the finite-time error-tracking problem in missile guidance, this paper presents a unified design approach through error dynamics and free-time convergence theory. The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics, characterized by its independence from initial conditions and guidance parameters, and adjustable convergence time. This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle, impact angle, and impact time. The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies. Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.

The quality of synthetic aperture radar (SAR) image degrades in the case of multiple imaging projection planes (IPPs) and multiple overlapping ship targets, and then the performance of target classification and recognition can be influenced. For addressing this issue, a method for extracting ship targets with overlaps via the expectation maximization (EM) algorithm is proposed. First, the scatterers of ship targets are obtained via the target detection technique. Then, the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP. Afterwards, a novel image amplitude estimation approach is proposed, with which the radar image of a single target with a single IPP can be generated. The proposed method can accomplish IPP selection and targets separation in the image domain, which can improve the image quality and reserve the target information most possibly. Results of simulated and real measured data demonstrate the effectiveness of the proposed method.

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.

Multi-objective optimization (MOO) for the microwave metamaterial absorber (MMA) normally adopts evolutionary algorithms, and these optimization algorithms require many objective function evaluations. To remedy this issue, a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions. An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations. Firstly, new sample points are generated by the MOO on surrogate models. Then, new samples are captured by exploiting each objective function. Furthermore, a weighted sum of the improvement of hypervolume (IHV) and the distance to sampled points is calculated to select the new sample. Compared with two well-known MOO algorithms, the proposed algorithm is validated by benchmark problems. In addition, two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.

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.

In the aircraft control system, sensor networks are used to sample the attitude and environmental data. As a result of the external and internal factors (e.g., environmental and task complexity, inaccurate sensing and complex structure), the aircraft control system contains several uncertainties, such as imprecision, incompleteness, redundancy and randomness. The information fusion technology is usually used to solve the uncertainty issue, thus improving the sampled data reliability, which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control system. In this work, we first analyze the uncertainties in the aircraft control system, and also compare different uncertainty quantitative methods. Since the information fusion can eliminate the effects of the uncertainties, it is widely used in the fault diagnosis. Thus, this paper summarizes the recent work in this aera. Furthermore, we analyze the application of information fusion methods in the fault diagnosis of the aircraft control system. Finally, this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends.

A generalized labeled multi-Bernoulli (GLMB) filter with motion mode label based on the track-before-detect (TBD) strategy for maneuvering targets in sea clutter with heavy tail, in which the transitions of the mode of target motions are modeled by using jump Markovian system (JMS), is presented in this paper. The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model. In update, we derive a tractable GLMB density, which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence (KLD), to enable the next recursive cycle. The relevant simulation results prove that the proposed multiple-model GLMB-TBD (MM-GLMB-TBD) algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background. Additionally, the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD (DP-TBD) algorithm. Meanwhile, the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD (JMS-MeMBer-TBD) filter in estimation error with the basically same computational cost. Finally, the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.

The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.

A new approach is proposed in this study for accountable capability improvement based on interpretable capability evaluation using the belief rule base (BRB). Firstly, a capability evaluation model is constructed and optimized. Then, the key sub-capabilities are identified by quantitatively calculating the contributions made by each sub-capability to the overall capability. Finally, the overall capability is improved by optimizing the identified key sub-capabilities. The theoretical contributions of the proposed approach are as follows. (i) An interpretable capability evaluation model is constructed by employing BRB which can provide complete access to decision-makers. (ii) Key sub-capabilities are identified according to the quantitative contribution analysis results. (iii) Accountable capability improvement is carried out by only optimizing the identified key sub-capabilities. Case study results show that “Surveillance”, “Positioning”, and “Identification” are identified as key sub-capabilities with a summed contribution of 75.55% in an analytical and deducible fashion based on the interpretable capability evaluation model. As a result, the overall capability is improved by optimizing only the identified key sub-capabilities. The overall capability can be greatly improved from 59.20% to 81.80% with a minimum cost of 397. Furthermore, this paper also investigates how optimizing the BRB with more collected data would affect the evaluation results: only optimizing “Surveillance” and “Positioning” can also improve the overall capability to 81.34% with a cost of 370, which thus validates the efficiency of the proposed approach.

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.

In this paper, a method for spoofing detection based on the variation of the signal’s carrier-to-noise ratio (CNR) is proposed. This method leverages the directionality of the antenna to induce varying gain changes in the signals across different incident directions, resulting in distinct CNR variations for each signal. A model is developed to calculate the variation value of the signal CNR based on the antenna gain pattern. This model enables the differentiation of the variation values of the CNR for authentic satellite signals and spoofing signals, thereby facilitating spoofing detection. The proposed method is capable of detecting spoofing signals with power and CNR similar to those of authentic satellite signals. The accuracy of the signal CNR variation value calculation model and the effectiveness of the spoofing detection method are verified through a series of experiments. In addition, the proposed spoofing detection method works not only for a single spoofing source but also for distributed spoofing sources.

Small video satellites have unique advantages of short development cycle, agile attitude maneuver, real-time video imaging. They have broad application prospects in space debris, faulty spacecraft, and other space target detection and tracking. However, when a space target first enters the camera’s visual field, it has a relatively large angular velocity relative to the satellite, which makes it easy to deviate from the visual field and cause off-target problems. This paper proposes a novel visual tracking control method based on potential function preventing missed targets in space. Firstly, a circular area in the image plane is designed as a mandatory restricted projection area of the target and a visual tracking controller based on image error. Then, a potential function is designed to ensure continuous and stable tracking of the target after entering the visual field. Finally, the stability of the control is proved using Barbarat’s lemma. By setting the same conditions and comparing with the simulation results of the proportion-derivative (PD) control method, the results show that when there is a large relative attitude motion angular velocity between the target and the satellite, the tracking method based on potential function can ensure that the target does not deviate from the field-of-view during the tracking control process, and the projection of target is controlled to the desired position. The proposed control method is effective in eliminating tracking error and preventing off-target simultaneously.

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.

A novel variable step-size modified super-exponential iteration (MSEI) decision feedback blind equalization (DFE) algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm. Based on the MSEI-DFE algorithm, it is first proposed to develop an error function as an improvement to the error function of MSEI, which effectively achieves faster convergence speed of the algorithm. Subsequently, a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero, so as to further improve the convergence speed of the algorithm. In the end, a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals. For the multipath underwater acoustic channel with mixed phase and phase rotation, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM) modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance. The results show that the proposed algorithm can considerably improve convergence speed and steady-state error, make effective compensation for phase rotation, and efficiently facilitate carrier recovery.

With the development of positioning technology, location services are constantly in demand by people. As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation. The pedestrian navigation based on radio is subject to environmental occlusion leading to the degradation of positioning accuracy. The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit (MIMU) is less susceptible to environmental interference, but its errors dissipate over time. In this paper, a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods. To suppress attitude errors, optimal feedback coefficients are established by pedestrian motion characteristics. To extend navigation time and improve positioning accuracy, the step length in subsequent movements is compensated by the first step length. The experimental results show that the positioning accuracy of the proposed method is improved by more than 47% and 44% compared with the pure inertia-based method combined with step compensation and the traditional complementary filtering combined method with step compensation. The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

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.

This paper considers the problem of sea clutter suppression. We propose the cuttable encoder-decoder-augmentation network (CEDAN) to improve clutter suppression performance by enriching the contrast information between the target and clutter. Specifically, the plug-and-play residual U-block (ResUblock) is proposed to augment the feature representation ability of the clutter suppression model. The CEDAN first extracts and fuses the multi-scale features using the encoder and the decoder composed of the ResUblocks. Then, the fused features are processed by the contrast information augmentation module (CIAM) to enhance the diversity of target and clutter, resulting in encouraging sea clutter suppression results. In addition, we propose the result-consistency loss to further improve the suppression performance. The result-consistency loss enables CEDAN to cut some blocks of decoder and CIAM to reduce the inference time without significantly degrading the suppression performance. Experimental results on measured and simulated data show that the CEDAN outperforms state-of-the-art sea clutter suppression methods in sea clutter suppression performance and computation efficiency.

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.

Piezo actuators are widely used in ultra-precision fields because of their high response and nano-scale step length. However, their hysteresis characteristics seriously affect the accuracy and stability of piezo actuators. Existing methods for fitting hysteresis loops include operator class, differential equation class, and machine learning class. The modeling cost of operator class and differential equation class methods is high, the model complexity is high, and the process of machine learning, such as neural network calculation, is opaque. The physical model framework cannot be directly extracted. Therefore, the sparse identification of nonlinear dynamics (SINDy) algorithm is proposed to fit hysteresis loops. Furthermore, the SINDy algorithm is improved. While the SINDy algorithm builds an orthogonal candidate database for modeling, the sparse regression model is simplified, and the Relay operator is introduced for piecewise fitting to solve the distortion problem of the SINDy algorithm fitting singularities. The Relay-SINDy algorithm proposed in this paper is applied to fitting hysteresis loops. Good performance is obtained with the experimental results of open and closed loops. Compared with the existing methods, the modeling cost and model complexity are reduced, and the modeling accuracy of the hysteresis loop is improved.

Blockchain technology has attracted worldwide attention, and has strong application potential in complex product system supply chain and other fields. This paper focuses on the supply chain management issues of complex product systems, and combines the technical characteristics of blockchain, such as tamper resistance and strong resistance to destruction, to conduct research on the application of blockchain based supply chain management for complex product systems. The blockchain technology is integrated into functional modules such as business interaction, privacy protection, data storage, and system services. The application technology architecture of complex product system supply chain integrated with blockchain is constructed. The application practice in complex product system supply chain is carried out. The results show that the supply chain of complex product systems has the functions of traceability, cost reduction, and anti-counterfeiting protection. Finally, the future development direction and research focus of the complex product system supply chain based on blockchain are prospected, which provides a reference for the equipment manufacturing supply chain management in the military industry.

Remote sensing data plays an important role in natural disaster management. However, with the increase of the variety and quantity of remote sensors, the problem of “knowledge barriers” arises when data users in disaster field retrieve remote sensing data. To improve this problem, this paper proposes an ontology and rule based retrieval (ORR) method to retrieve disaster remote sensing data, and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge, on this basis, and realizes the task suitability reasoning of earthquake disaster remote sensing data, mining the semantic relationship between remote sensing metadata and disasters. The prototype system is built according to the ORR method, which is compared with the traditional method, using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

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.

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.

Missile interception problem can be regarded as a two-person zero-sum differential games problem, which depends on the solution of Hamilton-Jacobi-Isaacs (HJI) equation. It has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation, and many iterative algorithms are proposed to solve the HJI equation. Simultaneous policy updating algorithm (SPUA) is an effective algorithm for solving HJI equation, but it is an on-policy integral reinforcement learning (IRL). For online implementation of SPUA, the disturbance signals need to be adjustable, which is unrealistic. In this paper, an off-policy IRL algorithm based on SPUA is proposed without making use of any knowledge of the systems dynamics. Then, a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is presented. Based on the online off-policy IRL method, a computational intelligence interception guidance (CIIG) law is developed for intercepting high-maneuvering target. As a model-free method, intercepting targets can be achieved through measuring system data online. The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

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.

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.

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.

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.

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.

The laser-guided bomb (LGB) is an air-to-ground precision-guided weapon that offers high hit rates, great power, and ease of use. LGBs are guided by semi-active laser ground-seeking technology, which means that atmospheric conditions can affect their accuracy. The spatial release region (SRR) of LGBs is difficult to calculate precisely, especially when there is a poor field of view. This can result in a lower real hit probability. To increase the hit probability of LGBs in tough atmospheric situations, a novel method for calculating the SRR has been proposed. This method is based on the transmittance model of the 1.06 μm laser in atmospheric species and the laser diffuse reflection model of the target surface to determine the capture target time of the laser seeker. Then, it calculates the boundary ballistic space starting position by ballistic model and gets the spatial scope of the spatial release region. This method can determine the release region of LGBs based on flight test data such as instantaneous velocity, altitude, off-axis angle, and atmospheric visibility. By more effectively employing aircraft release conditions, atmospheric visibility and other factors, the SRR calculation method can improve LGB hit probability by 9.2%.

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.

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.

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.

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.

Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems. However, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to implement. This paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also investigated. Several experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DL-based prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.

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.

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.