Performance-based warranties (PBWs) are widely used in industry and manufacturing. Given that PBW can impose financial burdens on manufacturers, rational maintenance decisions are essential for expanding profit margins. This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes, incorporating periodic inspection. A system performance degradation model is established. Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability. A benefits function, which includes incentives, is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds, thereby maximizing warranty profit. An improved sparrow search algorithm is developed to optimize the model, with a case study on large steam turbine rotor shafts. The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months, with subsequent intervals of about four months, and a preventive maintenance threshold of approximately 37.39 mm wear. When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals, the proposed PBW strategy increases the manufacturer’s profit by 1% and 18%, respectively. Sensitivity analyses provide managerial recommendations for PBW implementation. The PBW strategy proposed in this study significantly increases manufacturers’ profits by optimizing inspection intervals and preventive maintenance thresholds, and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.

The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems (CSoS), which can be abstracted as a heterogeneous combat network (HCN). It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS. To this end, this paper proposes an integrated framework called HCN disintegration based on double deep $Q$-learning (HCN-DDQL). Firstly, the enemy’s CSoS is abstracted as an HCN, and an evaluation index based on the capability and attack costs of nodes is proposed. Meanwhile, a mathematical optimization model for HCN disintegration is established. Secondly, the learning environment and double deep $Q$-network model of HCN-DDQL are established to train the HCN’s disintegration strategy. Then, based on the learned HCN-DDQL model, an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed. Finally, a case study is used to demonstrate the reliability and effectiveness of HCN-DDQL, and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.

This paper presents a quadcopter system for navigation in outdoor urban environments. The main contributions include the hardware design, the establishment of global occupancy grid maps based on millimeter-wave radars, the trajectory planning scheme based on optimal virtual tube methods, and the controller structure based on dynamics. The proposed system focuses on utilizing a compact and lightweight quadrotor with sensors to achieve navigation that conforms to the direction of urban roads with high computational efficiency and safety. Our work is an application of millimeter-wave radars and virtual tube planning for obstacle avoidance in navigation. The validness and effectiveness of the proposed system are verified by experiments.

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.

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.

A high precision detection technique is analyzed based on the optical micro electro-mechanical system (MEMS) accelerometer with double gratings for noise suppression and scale factor enhancement. The brief sensing model and modulation detection model are built using the phase sensitive detection, and the relationship between stimulated acceleration and system output is given. The schematics of gap modulation and light intensity modulation are analyzed respectively, and the choice of modulation frequency in the optical MEMS accelerometer system is discussed. According to the experimental results, the scale factor is improved from 15.45 V/g with the gap modulation to 18.78 V/g with the light intensity modulation, and the signal to noise ratio is improved from 42.95 dB to 81.73 dB. The overall noise level in the optical MEMS accelerometer is effectively suppressed.

In this paper, a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented, focusing on the challenges posed by sea clutter and multipath scattering. The performance of the radar detection methods under sea clutter, multipath, and combined conditions is categorized and summarized, and future research directions are outlined to enhance radar detection performance for low–altitude targets in maritime environments.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

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.

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning problem, a real-time UAV path planning algorithm based on long short-term memory (RPP-LSTM) network is proposed, which combines the memory characteristics of recurrent neural network (RNN) and the deep reinforcement learning algorithm. LSTM networks are used in this algorithm as Q-value networks for the deep Q network (DQN) algorithm, which makes the decision of the Q-value network has some memory. Thanks to LSTM network, the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment. Besides, the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning, so that the UAV can more reasonably perform path planning. Simulation verification shows that compared with the traditional feed-forward neural network (FNN) based UAV autonomous path planning algorithm, the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

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.

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.

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.

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.

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.

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 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.

Automatically recognizing radar emitters from complex electromagnetic environments is important but non-trivial. Moreover, the changing electromagnetic environment results in inconsistent signal distribution in the real world, which makes the existing approaches perform poorly for recognition tasks in different scenes. In this paper, we propose a domain generalization framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments. Specifically, we propose an end-to-end denoising based domain-invariant radar emitter recognition network (DDIRNet) consisting of a denoising model and a domain invariant representation learning model (IRLM), which mutually benefit from each other. For the signal denoising model, a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model. For the domain invariant representation learning model, contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distribution. Moreover, we design a data augmentation method that improves the diversity of signal data for training. Extensive experiments on classification have shown that DDIRNet achieves up to 6.4% improvement compared with the state-of-the-art radar emitter recognition methods. The proposed method provides a promising direction to solve the radar emitter signal recognition problem.

This paper presents a fixed-time cooperative guidance method with impact angle constraints for multiple flight vehicles (MFV) to address the challenges of intercepting large maneuvering targets with difficulty and low precision. A cooperative guidance model is proposed, transforming the cooperative interception problem into a consensus problem based on the remaining flight time of the flight vehicles. First, the impact angle constraint is converted into the line of sight (LOS) angle constraint, and a new fixed-time convergent non-singular terminal sliding surface is introduced, which resolves the singularity issue of the traditional sliding surfaces. With this approach, LOS angle rate and normal overloads can converge in fixed time, ensuring that the upper bound of the system convergence time is not affected by the initial value of the system. Furthermore, the maneuvering movement of the target is considered as a system disturbance, and an extended state observer is employed to estimate and compensate for it in the guidance law. Lastly, by applying consensus theory and distributed communication topology, the remaining flight time of each flight vehicle is synchronized to ensure that they intercept the target simultaneously with different impact angles. Simulation experiments are conducted to validate the effectiveness of the proposed cooperative interception and guidance method.

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.

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