This paper introduces a hybrid configuration design to enhance the precision of satellite antenna position measurement. By fixing the circular array antenna on the antenna mounting surface and integrating coordinate system transformation relationships with interferometric direction finding (DF) and positioning technology, accurate estimation of the antenna position is ensured. This method optimizes the quality and stability of data fusion by integrating pulse parameter characteristics, satellite orbit and attitude information, as well as the field of view information from observation stations, using techniques such as maximum-ratio-combining (MRC) and orbit extrapolation. Specifically, the sampling-importance resampling particle-filtering and Kalman-filtering (SIR-PF-KF) hybrid filtering prediction technology is employed to precisely predict and correct the three-dimensional (3D) position errors of the L-array antenna. Through data processing of five to nine orbits, accurate estimation of the antenna’s 3D position is achieved, achieving an estimation accuracy of 3 μm, significantly improving the accuracy of on-orbit rapid calibration. Experimental results show that the interferometer positioning accuracy is improved from 7.9 km before antenna position correction to within 0.2 km after correction, verifying the effectiveness and practicability of this method, which aims to address issues with positioning accuracy.

This paper investigates the high-performance control issues of systems affected by time-varying disturbances and measurement noise. Conventionally, active disturbance rejection control (ADRC) is a favorable control strategy to reject unknown disturbances and uncertainties. However, its control performance is limited because standard extended state observer (ESO) struggles to effectively estimate time-varying disturbances. The emergence of high-order ESO (HESO) alleviates the limitation. Unfortunately, it deteriorates the noise suppression capability when the disturbance rejection is enhanced. To tackle this challenge, an improved ADRC with cascade HESO (CHESO) is proposed. A comprehensive theoretical analysis associated with the performance of HESO is given for the first time. The presented analyses provide an intuitive understanding of the performance of HESO. Then, a novel CHESO is developed. The convergence of CHESO is proved via input-to-state stable theory. Extensive frequency domain analyses indicate that CHESO has stronger disturbance rejection and high-frequency noise attenuation performance than ESO and HESO without increasing the observer bandwidth. Comparative simulations conducted on a servo control system validate the effectiveness and preponderance of the proposed method.

The precision and quality of machining in computer numerical control (CNC) machines are significantly impacted by the state of the tool. Therefore, it is essential and crucial to monitor the tool’s condition in real time during operation. To improve the monitoring accuracy of tool wear values, a tool wear monitoring approach is developed in this work, which is based on an improved integrated model of densely connected convolutional network (DenseNet) and gated recurrent unit (GRU), which incorporates data preprocessing via wavelet packet transform (WPT). Firstly, wavelet packet decomposition (WPD) is used to extract time-frequency domain features from the original time-series monitoring signals of the tool. Secondly, the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels, and feature fusion is performed. A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates. Finally, the GRU is utilized to extract temporal features from the extracted deep-level signal features, and the feature mapping of these temporal features is then carried out by a fully connected neural network, which ultimately achieves the monitoring of tool wear values. Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms.

This paper investigates the distributed continuous-time aggregative optimization problem for second-order multi-agent systems, where the local cost function is not only related to its own decision variables, but also to the aggregation of the decision variables of all the agents. By using the gradient descent method, the distributed average tracking (DAT) technique and the time-base generator (TBG) technique, a distributed continuous-time aggregative optimization algorithm is proposed. Subsequently, the optimality of the system’s equilibrium point is analyzed, and the convergence of the closed-loop system is proved using the Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.

To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious operations, this paper proposes an integrated framework combining complex system network modeling with dynamic adversarial simulation for evaluating mission-critical system-of-systems (SoS). Specifically, the contribution rate of unmanned aerial vehicles (UAVs) to the amphibious joint landing SoS (AJLSoS) is quantified. Firstly, a standardized network topology model is developed using operation loop theory, systematically characterizing node functionalities and their interdependencies. Secondly, the ideal Lanchester equation is augmented according to the model’s static operational capability, and an amphibious operational simulation model is constructed based on the modified equation, enabling dynamic simulation of force attrition and engagement duration as key performance indicators of AJLSoS. To validate the theoretical framework, a battalion-level amphibious campaign scenario is developed to compute effectiveness metrics across multiple control scenarios and the contribution rate of UAVs to AJLSoS is analyzed. This study not only provides actionable insights for operational mission planning of UAVs in the context of amphibious operations but also demonstrates high adaptability to diverse operational contexts.

During actual high-speed flights, the electromagnetic (EM) properties of aircraft radomes are influenced by dielectric temperature drift, leading to substantial drift in the boresight errors (BSEs) from their room temperature values. However, applying thermal loads to the radome during ground-based EM simulation tests is challenging. This paper presents an EM equivalent physical model (EEPM) for high-speed aircraft radomes that account for the effects of dielectric temperature drift. This is achieved by attaching dielectric slices of specific thicknesses to the outer surface of a room-temperature radome (RTR) to simulate the increase in electrical thickness resulting from high temperatures. This approach enables accurate simulations of the BSEs of high-temperature radomes (HTRs) under high-speed flight conditions. An application example, supported by full-wave numerical calculations and physical testing, demonstrates that the EEPM exhibits substantial improvement in approximating the HTR compared to the RTR, facilitating precise simulations of the BSEs of HTRs during high-speed flights. Overall, the proposed EEPM is anticipated to considerably enhance the alignment between the ground-based simulations of high-speed aircraft guidance systems and their actual flight conditions.

In the traditional radar unmanned aerial vehicle (UAV) detection process, coherent integration and micro-Doppler (m-D) parameter estimation are carried out separately. The target discrimination process needs to obtain the position information of the target, which will lose energy. In this paper, a long time integration method of radar signal based on rotating target radon Fourier transform (RTRFT) is proposed. This method modifies the distance and frequency terms in the traditional generalized radon Fourier transform (GRFT), and adds the frequency sinusoidal modulation term. Then, based on the cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, the position of the target is detected in the high-dimensional space obtained by RTRFT. This method can combine coherent integration and micro-motion parameter estimation. Simulation experiments show that the proposed method can estimate the main translational parameters and rotational micro-motion parameters of the target while detecting the target, and the target detection performance is improved.

Agile earth observation satellites (AEOSs) represent a new generation of satellites with three degrees of freedom (pitch, roll, and yaw); they possess a long visible time window (VTW) for ground targets and support imaging at any moment within the VTW. However, different observation times demonstrate different cloud cover distributions, which exhibit different effects on the AEOS observation. Previous studies ignored pitch angles, discretized VTWs, or fixed cloud cover for every VTW, which led to the loss of intermediate observation states, thus these studies are not suitable for AEOS scheduling considering cloud cover distribution. In this study, a relationship formula between the cloud cover and observation time is proposed to calculate the cloud cover for every observation time, and a relationship formula between the observation time and pitch angle is designed to calculate the pitch angle for every observation time in the VTW. A refined model including the pitch angle, roll angle, and cloud cover distribution is established, which can make the scheme closer to the actual application of AEOSs. A hybrid genetic simulated annealing (HGSA) algorithm for AEOS scheduling is proposed, which integrates the advantages of genetic and simulated annealing algorithms and can effectively avoid falling into a local optimal solution. The experiments are conducted to compare the proposed algorithm with the traditional algorithms, the results verify that the proposed model and algorithm are efficient and effective for AEOS scheduling considering cloud cover distribution.

As the Mars probe, which has limited on-board ability in computation is unable to carry out the large-scale landmark solution, it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigation accuracy during landing phase. This paper proposes an optimal landmark selection method based on the observability matrix for the Mars probe. Firstly, an observability matrix for navigation system is constructed with Fisher information quantity. Secondly, the optimal configuration of the landmark distribution is given by maximizing the scalar function of the observability matrix. Based on the optimal configuration, the greedy algorithm is used to determine the number of the landmarks at each moment adaptively. In addition, considering the fact that the number of the observable landmarks gradually decreases during the landing process, the convergence threshold of the greedy algorithm is set to a dynamic value regarding landing time. Finally, mathematical simulation verification is conducted, and the results show that the proposed optimal landmark selection method has higher navigation accuracy compared with the random landmark selection method. It can effectively suppress the influence of the measurement model errors and achieve a higher landing accuracy.

A cooperative guidance law is proposed in a two-on-two engagement scenario with large-heading-errors by choosing zero-effort miss distance as a sliding surface, which consists of an attacker, a protector, a defender, and a target, based on fixed-time sliding mode control theory. Based on the nonlinear method of fixed-time sliding mode control, the performance of the cooperative guidance law remains satisfactory even with large-heading-errors scenarios where the linearization-based approaches might be invalid. By virtue of this law, the attacker pursues the target with the assistance of the protector, which can intercept the defender in the engagement scenario. Furthermore, if the attacker is intercepted by the defender, the guidance law of the protector could guarantee that the protector attacks the target. A robust adaptive term is included in the guidance law to deal with the case of the unknown disturbance upper bound of the defender-target team. Finally, the feasibility of the guidance law is verified by nonlinear numerical simulations, and the superiority of it is illustrated by comparing with the linearization guidance law.

This paper proposes a differential-fatness-based active disturbance rejection control (ADRC) for high-speed steering control of tracked tank systems. Firstly, a high-speed steering model is established by considering the lateral component of the centrifugal force acting on the tank on the basis of modeling and analyzing the dynamic model of the low-speed steering system. Secondly, we propose a differential-flatness ADRC approach by converting the under-actuated system to a fully driven flat one. Moreover, we prove the differential flatness of the steering system, which facilitates a two-channel ADRC development. Finally, we show that both the states of the flat system and the original under-actuated system can track the reference trajectory. On the external interference condition, the system is observed to re-track the target signal within 2 s.

In an aircraft final assembly line (AFAL), the rational scheduling of assembly workers to complete tasks in an orderly manner is crucial for enhancing production efficiency. This paper addresses the multi-skilled worker scheduling problem in the AFAL, where the processing time of each task varies due to the assigned workers’ skill levels, referred to as variable duration. The objective is to minimize the makespan, i.e., the total time required for all workers to complete all tasks. A mixed integer linear programming model is formulated under complex constraints including assembly precedence relations, skill requirements, worker skill capabilities, and workspace capacities. To solve the model effectively, a multi-pass priority rule-based heuristic (MPRH) algorithm is proposed. This algorithm integrates 14 activity priority rules and nine worker priority rules with worker weights. Extensive experiments iteratively the best-performing priority rules, and the most effective rule subsets are integrated through a lightweight multi-pass mechanism to enhance its efficiency. The computational results demonstrate that the MPRH can find high-quality solutions effectively within very short central processing unit central processing unit (CPU) time compared to GUROBI. A case study based on real data obtained from an AFAL confirms the necessity and the feasibility of the approach in practical applications. Sensitivity analyses provide valuable insights to real production scenarios.

Aiming at the characteristics of autonomy, confrontation, and uncertainty in unmanned aerial vehicle (UAV) swarm operations, case-based reasoning (CBR) technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced, and a recommendation method for UAV swarm operation strategies based on CBR is proposed. Firstly, we design a universal framework for UAV swarm operation strategies from three dimensions: operation effectiveness, time, and cost. Secondly, based on the representation of operation cases, certain, fuzzy, interval, and classification attribute similarity calculation methods, as well as entropy-based attribute weight allocation methods, are suggested to support the calculation of global similarity of cases. This method is utilized to match the source case with the most similarity from the historical case library, to obtain the optimal recommendation strategy for the target case. Finally, in the form of red blue confrontation, a UAV swarm operation strategy recommendation case is constructed based on actual battle cases, and a system simulation analysis is conducted. The results show that the strategy given in the example performs the best in three evaluation indicators, including cost-effectiveness, and overall outperforms other operation strategies. Therefore, the proposed method has advantages such as high real-time performance and interpretability, and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes. At the same time, this study could also provide new ideas for the selection of UAV swarm operation strategies.

Electronic reconnaissance units commonly utilize an interferometer direction-finding system to measure the incoming direction of radar radiation signals. This approach enables the accurate determination of threat source locations, which is essential for devising route plans oriented toward flight path generation. When a frequency diverse array (FDA) system is adopted by ground radars, errors are introduced into the angle measurements of the passive direction finding system. To address this issue, this study starts with FDA model establishment and equiphasic surface characteristics analysis and analyzes the principles of FDA deception in identifying one-dimensional single-baseline interferometer directions. Additionally, the Cramer-Rao bounds of the signal carrier frequency estimation error and angle measurement error during the interferometer’s direction finding process are considered. The simulation results verify that the one-dimensional single-baseline interferometer direction finding system can be deceived by the FDA radar, and the FDA with a sine frequency offset exhibits the optimum deception effect.

Exact estimation of space object attitude parameters is a great challenge. The effectiveness of conventional attitude estimation approaches based on target sizes suffers a significant reduction when occlusion exists. This paper proposes an innovative approach to estimate the attitude parameters for space objects based on inverse synthetic aperture radar (ISAR) image sequences. The formulation for nonlinear size constraints (NSC) is developed by accounting for the characteristics of object size variation in ISAR image sequences. The multi-start framework for global optimization and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton iterative method are combined with and used for more accurate estimation of space object’s attitude parameters. Furthermore, the Cramer-Rao lower bound (CRLB) of attitude parameter estimates is derived. Comparative experiments demonstrate the effectiveness and robustness of the proposed method.

Aiming at the terminal defense problem of aircraft, this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position. The method employs a λ-return based reinforcement learning algorithm, which can be applied to the flight assistance decision-making system to improve the pilot’s survivability. First, we model the environment to simulate the interaction between air-to-air missiles and aircraft. Subsequently, we propose a λ-return based approach to improve the deep Q learning network (DQN), deep advantageous actor criticism (A2C), and proximity policy optimization (PPO) algorithms used to train manoeuvre strategies. The method employs an action space containing nine manoeuvres and defines the off-target distance at the end of the scene as a sparse reward for algorithm training. Simulation results show that the convergence speed of the three improved algorithms is significantly improved when using the λ-return method. Moreover, the effect of the fetch value on the convergence speed is verified by ablation experiments. In order to solve the illegal behavior problem in the training process, we also design a backtracking-based illegal behavior masking mechanism, which improves the data generation efficiency of the environment model and promotes effective algorithm training.

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.

In this paper, a grey Kalman filter model is proposed for lithium battery charge state estimation. Firstly, this paper establishes a recursive relation equation between the front and back terms through the grey model (GM). Secondly, the state space expression is constructed based on the recursive relationship equation. Next, the Kalman filter algorithm is integrated to form a grey Kalman filter model. Finally, the charge state is estimated based on public lithium battery data. In this paper, the state of charge is estimated from three different aspects, including different driving cycles, randomly mixed driving cycles, and the estimation of the state of charge by different temperatures under the same driving cycle conditions. On this basis, the model is applied to a life scenario using the charge state of 20 electric vehicles. The results show that the proposed model has good accuracy.

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

As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temperature sensitivity of optical devices, the influence of environmental temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learning based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors generated in the fiber ring due to the Shupe effect. This work proposes a composite model based on k-means clustering, support vector regression, and particle swarm optimization algorithms. And it significantly reduced redundancy within the samples by adopting the interval sequence sample. Moreover, metrics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effectiveness. This work effectively enhances the consistency between data and models across different temperature ranges and temperature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utilizing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guidance and technical references for sensors error compensation work in other fields.

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.

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.

Two-photon fluorescence microscopy, based on the principles of two-photon excited fluorescence and second harmonic generation, enables real-time non-invasive in vivo imaging of skin and cells, providing a means to assess human health status. In this paper, a miniaturized two-photon imaging system is designed and fabricated to withstand extreme vibration and shock environments. The mechanical stability of the optical and structural components of the miniaturized probe is evaluated under random vibration and shock vibration tests using finite element simulation methods and ray tracing techniques. During the environmental testing, the maximum stress on the probe is 11.5 MPa, which is well below the threshold for structural failure. The largest structural displacement occurs at the collimator, where random vibrations produce an offset of 10.9 μm. This offset is analyzed by using geometric optics and point spread functions. Under the maximum collimator offset, the theoretical resolution, as calculated by the point spread function, shifted from 463.28 nm to 463.48 nm. Additionally, a lateral offset of 127 nm is observed at the center position, which does not significantly impact the imaging performance. Finally, environmental and imaging performance tests are conducted. The system’s measured resolution after the environmental tests is 530 nm, consistent with its resolution prior to testing. Imaging tests are also performed on the skin’s stratum corneum, granular layer, spinous layer, and basal cell layer, revealing clear cellular structural information. These results confirm the device’s potential for applications in extreme shock and vibration environments.

To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper proposed multi-operator real-time constraints particle swarm optimization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and integrates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evolution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection optimization of paths, gradually reducing algorithmic space, accelerating convergence, and enhances path cooperativity. Simulation experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demonstrate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. Moreover it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collaborative path planning. The experiments are conducted in three collaborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.

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

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%.

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

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

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

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.

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.

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.

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.

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

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