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.

A methodology for the reduction of radar cross section (RCS) of cambered platforms within the target airspace is presented, which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface. By applying the theory of generalized Brewster complex wave impedance matching, five distinct unit cell designs are developed to attain more than 95% absorption rate for dual-polarized incident waves within five angular ranges: 0°?30°, 30°?50°, 50°?60°, 60°?70°, and 70°?80°. To optimally reduce the RCS of a cambered platform, the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace. As an illustrative example, the leading edge of an airfoil is taken into account, and experimental measurements validate the efficiency of the proposed structure. Specifically, the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz (about 66.7% relative bandwidth) for dual polarizations.

In wireless sensor networks, ensuring communication security via specific emitter identification (SEI) is crucial. However, existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform class-incremental training. This study proposes a class-incremental open-set SEI approach. The open-set SEI model calculates radio-frequency fingerprints (RFFs) prototypes for known signals and employs a self-attention mechanism to enhance their discriminability. Detection thresholds are set through Gaussian fitting for each class. For class-incremental learning, the algorithm freezes the parameters of the previously trained model to initialize the new model. It designs specific losses: the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss, which force the new model to retain old knowledge while learning new knowledge. The training loss enables learning of new class RFFs. Experimental results demonstrate that the open-set SEI model achieves state-of-the-art performance and strong noise robustness. Moreover, the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge, acquire new device RFFs knowledge, and detect unknown devices simultaneously.

Multichannel signals have the characteristics of information diversity and information consistency. To better explore and utilize the affinity relationship within multichannel signals, a new graph learning technique based on low rank tensor approximation is proposed for multichannel monitoring signal processing and utilization. Firstly, the affinity relationship of multichannel signals can be acquired based on the clustering results of each channel signal. Wherein an affinity tensor is constructed to integrate the diverse and consistent information of the clustering information among multichannel signals. Secondly, a low-rank tensor optimization model is built and the joint affinity matrix is optimized with the assistance of the strong confidence affinity matrix. Through solving the optimization model, the fused affinity relationship graph of multichannel signals can be obtained. Finally, the multichannel fused clustering results can be acquired though the updated joint affinity relationship graph. The multichannel signal utilization examples in health state assessment with public datasets and microwave detection with actual echoes verify the advantages and effectiveness of the proposed method.

For mission-oriented unmanned aerial vehicle (UAV) swarms, mission capability assessment provides an important reference in the design and development process, and is a precondition for mission success. For this multi-criteria decision-making (MCDM) problem, the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process (ANP) approaches neglect the hesitancy of subjective judgments. To fill these research gaps, an MCDM method based on unified architecture framework (UAF) and interval-valued spherical fuzzy ANP (IVSF-ANP) is proposed in this paper. Firstly, selected viewpoints in UAF are extended to construct criteria models with standardized representation. Secondly, interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria, handling fuzziness and hesitancy in pairwise comparisons. A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts. Next, performance characteristics are non-linearly transformed regarding to expectations to get final results. This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels. Comparative analysis shows that the proposed method is valid and reasonable.

Manned aerial vehicle-unmanned aerial vehicle (MAV-UAV) combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology, and the generation of force formation plan is a key step in the organizational planning. Based on the description of the problem and the definition of organizational elements, the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability, resource capability and the number of platforms used. Based on the non-dominated sorting genetic algorithm III (NSGA-III) framework, which includes encoding/decoding method and constraint handling method, the generation model of organizational force formation plan is solved, and the effectiveness and superiority of the algorithm are verified by simulation experiments.

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.

Formation control of multiple spacecraft has attracted extensive research attention. However, achieving reliable performance under sensor failures remains a significant challenge. This paper develops an integrated framework that jointly designs distributed observers and local controllers to ensure robust formation control in the presence of external disturbances and sensor malfunctions. Treating the spacecraft formation as a single interconnected system, each spacecraft constructs a distributed observer that estimates the overall system state by incorporating both its own measurements and the predicted control information shared among the spacecraft. Based on the observer estimates, a local control law is synthesized to maintain the desired formation. Rigorous theoretical analysis and numerical simulations demonstrate that the proposed integrated approach effectively guarantees formation stability and resilience against sensor failures and disturbances.

Mega low Earth orbit (LEO) satellite networks serve as effective complements to terrestrial networks. However, the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users. Moreover, there are both ascending and descending segments in widely deployed walker-delta constellations. Even if the locations of users do not change, when the access satellites of the communicating parties are not in the same ascending or descending segment, the end-to-end latency between them will increase. To address this challenge, the self-decision handover (SDH) strategy and the joint decision handover (JDH) strategy are proposed, and they both incorporate the routing hops as a crucial handover criterion to minimize the end-to-end latency. In addition, the shortest route hop-count algorithm is designed to assist in the handover decision-making process. Simulations demonstrate that the proposed handover strategies outperform the traditional handover strategies in terms of the number of handovers and end-to-end latency.

Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature (I/Q) signals, challenges persist due to signal-type confusion and background noise interference. To address those limitations, this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network (MCPC-MCVResNet) framework. This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences, effectively capturing both amplitude and phase information within I/Q data. A core innovation of this approach is the sphere space softmax (SS-softmax) loss, which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters. The SS-softmax mechanism significantly enhances the model’s capacity to discern subtle variations among radiation emitters. Experimental results demonstrate superior identification accuracy, rapid convergence, and exceptional robustness in low signal-to-noise ratio environments.

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments. The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern. Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns, with insufficient exploration of techniques for managing nonstationary beam directions. To address this gap, this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response (MVDR) beamformer. Building on this, the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem, which is then resolved using the cuckoo search (CS) algorithm. Simulations confirm the effectiveness of the proposed approach, showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.

Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain. However, current methods lack measurable and interpretable metrics. To address this issue, this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral, which is based on a classification confidence-based confusion matrix, offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms, and enhances intuitiveness and interpretability of attack impacts. We first conduct a validity test and sensitive analysis of the method. Then, prove its effectiveness through the experiments of five classification algorithms including artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), convolutional neural network (CNN) and transformer against three adversarial attacks such as fast gradient sign method (FGSM), DeepFool, and projected gradient descent (PGD) attack.

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.

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.

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, the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output (TS-MIMO) radar is investigated, aiming to enhance its low probability of intercept (LPI) capability. The main objective is to simultaneously minimize the transmission power, suppress the transmit sidelobe levels, and minimize the probability of intercept, thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance. An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers, yielding an unified LPI optimization framework. Particularly, the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method. Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.