Nowadays, wireless communication devices turn out to be transportable owing to the execution of the current technologies. The antenna is the most important component deployed for communication purposes. The antenna plays an imperative role in receiving and transmitting the signals for any sensor network. Among varied antennas, micro strip fractal antenna (MFA) significantly contributes to increasing antenna gain. This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design. This method optimizes antenna characteristics, including directivity and gain. Here, the factors, including length, width, ground plane length, height, and feed offset-X and feed offset-Y, are taken into account to achieve the best performance of gain and directivity. Ultimately, the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain. The adopted model converges to a minimal value of 0.2872. Further, the spider monkey optimization (SMO) model accomplishes the worst performance over all other existing models like elephant herding optimization (EHO), grey wolf optimization (GWO), lion algorithm (LA), support vector regressor (SVR), bacterial foraging–particle swarm optimization (BF-PSO) and shark smell optimization (SSO). Effective MFA design is obtained using the suggested strategy regarding various parameters.

This work proposes the application of an iterative learning model predictive control (ILMPC) approach based on an adaptive fault observer (FOBILMPC) for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles. In order to increase the control amount, this online control legislation makes use of model predictive control (MPC) that is based on the concept of iterative learning control (ILC). By using offline data to decrease the linearized model’s faults, the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed. An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree. During the derivation process, a linearized model of longitudinal dynamics is established. The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.

To meet the requirements of modern air combat, an integrated fire/flight control (IFFC) system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden. Considering the complex dynamic characteristics and the couplings of armed helicopters, an improved automatic attack system is constructed to integrate the fire control system with the flight control system into a unit. To obtain the optimal command signals, the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno (C-BFGS) algorithm combined with the trust region method. To address the uncertainties in the automatic attack system, the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties, and the dual solvable problem is analyzed by using the duality theory, conjugate function, and dual norm. Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accuracy.

To address the confrontation decision-making issues in multi-round air combat, a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle (UAV) air combat. Based on game theory and the confrontation characteristics of air combat, a dynamic game process is constructed including the strategy sets, the situation information, and the maneuver decisions for both sides of air combat. By analyzing the UAV’s flight dynamics and the both sides’ information, a payment matrix is established through the situation advantage function, performance advantage function, and profit function. Furthermore, the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution, where the decision tree method is introduced to obtain the optimal maneuver decision, thereby improving the situation advantage in the next round of confrontation. According to the analysis, the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advantages of the proposed method.

Multi-agent systems often require good interoperability in the process of completing their assigned tasks. This paper first models the static structure and dynamic behavior of multi-agent systems based on layered weighted scale-free community network and susceptible-infected-recovered (SIR) model. To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors, a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems. A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm. A method for evaluating system interoperability is designed based on simulation experiments, providing reference for the construction planning and optimization of organizational application of the system. Finally, the feasibility of the method is verified through case studies.

Nonperiodic interrupted sampling repeater jamming (ISRJ) against inverse synthetic aperture radar (ISAR) can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation, which is obviously different from the conventional multi-false-target deception jamming. In this paper, a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory. By utilizing the discontinuous property of the jamming in slow time domain, the unjammed pulse is separated using the intra-pulse energy function difference. Based on this, the two-dimensional orthogonal matching pursuit (2D-OMP) algorithm is proposed. Further, it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence. The validity of the proposed method is demonstrated via the Yake-42 plane data simulations.

Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies. However, the data from these projects is often complex and inadequate, making it challenging for researchers to conduct in-depth data mining to improve policies or management. To address this problem, this paper adopts a top-down approach to construct a knowledge graph (KG) for research projects. Firstly, we construct an integrated ontology by referring to the metamodel of various architectures, which is called the meta-model integration conceptual reference model. Subsequently, we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities, completing the construction of the KG for the research projects. In addition, a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG. Finally, experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.

In order to get rid of the dependence on high-precision centrifuges in accelerometer nonlinear coefficients calibration, this paper proposes a system-level calibration method for field condition. Firstly, a 42-dimension Kalman filter is constructed to reduce impact brought by turntable. Then, a biaxial rotation path is designed based on the accelerometer output model, including orthogonal 22 positions and tilt 12 positions, which enhances gravity excitation on nonlinear coefficients of accelerometer. Finally, sampling is carried out for calibration and further experiments. The results of static inertial navigation experiments lasting 4000 s show that compared with the traditional method, the proposed method reduces the position error by about 390 m.

The influence of ocean environment on navigation of autonomous underwater vehicle (AUV) cannot be ignored. In the marine environment, ocean currents, internal waves, and obstacles are usually considered in AUV path planning. In this paper, an improved particle swarm optimization (PSO) is proposed to solve three problems, traditional PSO algorithm is prone to fall into local optimization, path smoothing is always carried out after all the path planning steps, and the path fitness function is so simple that it cannot adapt to complex marine environment. The adaptive inertia weight and the “active” particle of the fish swarm algorithm are established to improve the global search and local search ability of the algorithm. The cubic spline interpolation method is combined with PSO to smooth the path in real time. The fitness function of the algorithm is optimized. Five evaluation indexes are comprehensively considered to solve the three-demensional (3D) path planning problem of AUV in the ocean currents and internal wave environment. The proposed method improves the safety of the path planning and saves energy.

Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.

The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI) to take advantage of the massive multiple-input multiple-output (MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN) neural network-based method that is used to solve this problem. Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then, the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN) with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.

In this paper, we propose an improved YOLOv5-based object detection method for radar images, which have the characteristics of diffuse weak noise and imaging distortion. To mitigate the effects of noise without losing spatial information, an coordinate attention (CA) has been added to pre-extract the feature of the images, which can guarantee a better feature extraction ability. A new stochastic weighted average (SWA) method is designed to refine generalization ability of the algorithm, where the medium mean is used instead of their average value. By introducing an deformable convolution, both regular and irregular images can be proceeded. The experimental results show that the improved algorithm performs better in object detection of radar images compared with the YOLOv5 model, which confirms the effectiveness and feasibility of our model.

As commercial drone delivery becomes increasingly popular, the extension of the vehicle routing problem with drones (VRPD) is emerging as an optimization problem of interests. This paper studies a variant of VRPD in multi-trip and multi-drop (VRP-mmD). The problem aims at making schedules for the trucks and drones such that the total travel time is minimized. This paper formulate the problem with a mixed integer programming model and propose a two-phase algorithm, i.e., a parallel route construction heuristic (PRCH) for the first phase and an adaptive neighbor searching heuristic (ANSH) for the second phase. The PRCH generates an initial solution by concurrently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase. Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase. Numerical tests on some benchmark data are conducted to verify the performance of the algorithm. The results show that the proposed algorithm can found better solutions than some state-of-the-art methods for all instances. Moreover, an extensive analysis highlights the stability of the proposed algorithm.

High complexity and uncertainty of air combat pose significant challenges to target intention prediction. Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns. Accordingly, this study proposes a Mogrifier gate recurrent unit-D (Mog-GRU-D) model to address the combat target intention prediction issue under the incomplete information condition. The proposed model directly processes missing data while reducing the independence between inputs and output states. A total of 1200 samples from twelve continuous moments are captured through the combat simulation system, each of which consists of seven dimensional features. To benchmark the experiment, a missing valued dataset has been generated by randomly removing 20% of the original data. Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25% when dealing with incomplete information. This study provides possible interpretations for the principle of target interactive mechanism, highlighting the model’s effectiveness in potential air warfare implementation.

Project construction and development are an important part of future army designs. In today’s world, intelligent warfare and joint operations have become the dominant developments in warfare, so the construction and development of the army need top-down, top-level design, and comprehensive planning. The traditional project development model is no longer sufficient to meet the army’s complex capability requirements. Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effectiveness. At the same time, when a program consists of large-scale project data, the effectiveness of the traditional, precise mathematical planning method is greatly reduced because it is time-consuming, costly, and impractical. To solve above problems, this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algorithm and verifies the effectiveness and feasibility of the model and algorithm through an example. The results show that the hybrid algorithm proposed in this paper is better than the existing meta-heuristic algorithm.

This paper considers the short-range sensing implementation in continuous-wave (CW) phased array systems. We specifically address this CW short-range sensing challenges stemming from the self-interference cancellation (SIC) operation and synthesis requirement of arbitrary beampatterns for the sensing purpose, which has rarely been researched before. In this paper, unlike the only existed work that exploits the heuristic method and shares no analytical solution, an SIC pattern synthesis design is presented with a closed-form solution. By utilizing the null-space projection (NSP) method, the proposed method effectively mitigates the self-interference to enable the in-band full-duplex operation of the array system. Subsequently, the NSP design will be innovatively embedded in a singular value decomposition (SVD) based weighted alternating reserve projection (WARP) approach to efficiently synthesize an arbitrary desired pattern by solving a unique rank-deficient weighted least mean square problem. Numerical results validate the effectiveness of the proposed method in terms of beampattern, SIC performance, and sensing performance.

This paper proposes a reliability evaluation model for a multi-dimensional network system, which has potential to be applied to the internet of things or other practical networks. A multi-dimensional network system with one source element and multiple sink elements is considered first. Each element can connect with other elements within a stochastic connection ranges. The system is regarded as successful as long as the source element remains connected with all sink elements. An importance measure is proposed to evaluate the performance of non-source elements. Furthermore, to calculate the system reliability and the element importance measure, a multi-valued decision diagram based approach is structured and its complexity is analyzed. Finally, a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the element importance measure.

Developing intelligent unmanned swarm systems (IUSSs) is a highly intricate process. Although current simulators and toolchains have made a notable contribution to the development of algorithms for IUSSs, they tend to concentrate on isolated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner. Furthermore, the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms. Therefore, a comprehensive solution must be developed that encompasses the entire IUSS development lifecycle. In this study, we present the RflySim ToolChain, which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs. The RflySim ToolChain employs a model-based design (MBD) approach, integrating a modeling and simulation module, a lower reliable control module, and an upper swarm decision-making module. This comprehensive integration encompasses the entire process, from modeling and simulation to testing and deployment, thereby enabling users to rapidly construct and validate IUSSs. The principal advantages of the RflySim ToolChain are as follows: it provides a comprehensive solution that meets the full-stack development needs of IUSSs; the highly modular architecture and comprehensive software development kit (SDK) facilitate the automation of the entire IUSS development process. Furthermore, the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment, which is known as the simulation to reality (Sim2Real) process. This paper presents a series of case studies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.

To enhance direction of arrival (DOA) estimation accuracy, this paper proposes a low-cost method for calibrating far-field steering vectors of large aperture millimeter wave radar (mmWR). To this end, we first derive the steering vectors with amplitude and phase errors, assuming that mmWR works in the time-sharing mode. Then, approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed, which is used to accomplish the mapping between the two of them. Finally, simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.

In low Earth orbit (LEO) satellite networks, on-board energy resources of each satellite are extremely limited. And with the increase of the node number and the traffic transmission pressure, the energy consumption in the networks presents uneven distribution. To achieve energy balance in networks, an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor (DEF) is proposed. The DEF is defined as the function of the inter-satellite link distance and the cumulative network energy consumption ratio. According to the minimum sum of DEF on inter-satellite links, an energy consumption balancing algorithm based on DEF is proposed, which can realize dynamic traffic transmission optimization of multiple traffic services. It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network, make full use of idle nodes with low energy consumption, and optimize the energy consumption distribution of the whole network according to the continuous iterations of each traffic service flow. Simulation results show that, compared with the traditional shortest path algorithm, the proposed method can improve the balancing performance of nodes by 75% under certain traffic pressure, and realize the optimization of energy consumption balancing of the whole network.

The multifunctional integration system (MFIS) is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet different operational requirements. Dwell scheduling is a key for the system to realize multifunction and maximize the resource utilization. In this paper, an adaptive dwell scheduling optimization model for MFIS which considers the aperture partition and joint radar communication (JRC) waveform is established. To solve the formulated optimization problem, JRC scheduling conditions are proposed, including time overlapping condition, beam direction condition and aperture condition. Meanwhile, an effective mechanism to dynamically occupy and release the aperture resource is introduced, where the time-pointer will slide to the earliest ending time of all currently scheduled tasks so that the occupied aperture resource can be released timely. Based on them, an adaptive dwell scheduling algorithm for MFIS with aperture partition and JRC waveform is put forward. Simulation results demonstrate that the proposed algorithm has better comprehensive scheduling performance than up-to-date algorithms in all considered metrics.

The operational readiness test (ORT), like weapon testing before firing, is becoming more and more important for systems used in the field. However, the test requirement of the ORT is distinctive. Specifically, the rule of selecting test items should be changed in different test turns, and whether there is a fault is more important than where the fault is. The popular dependency matrix (D-matrix) processing algorithms becomes low efficient because they cannot change their optimizing direction and spend unnecessary time on fault localization and isolation. To this end, this paper proposes a D-matrix processing algorithm named piecewise heuristic algorithm for D-matrix (PHAD). Its key idea is to use a piecewise function comprised of multiple different functions instead of the commonly used fixed function and switch subfunctions according to the test stage. In this manner, PHAD has the capability of changing optimizing direction, precisely matching the variant test requirements, and generating an efficient test sequence. The experiments on the random matrixes of different sizes and densities prove that the proposed algorithm performs better than the classical algorithms in terms of expected test cost (ETC) and other metrics. More generally, the piecewise heuristic function shows a new way to design D-matrix processing algorithm and a more flexible heuristic function to meet more complicated test requirements.

The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimization problem. In order to solve the resulting optimization problem, the dwell scheduling process in a scheduling interval (SI) is formulated as a Markov decision process (MDP), where the state, action, and reward are specified for this dwell scheduling problem. Specially, the action is defined as scheduling the task on the left side, right side or in the middle of the radar idle timeline, which reduces the action space effectively and accelerates the convergence of the training. Through the above process, a model-free reinforcement learning framework is established. Then, an adaptive dwell scheduling method based on Q-learning is proposed, where the converged Q value table after training is utilized to instruct the scheduling process. Simulation results demonstrate that compared with existing dwell scheduling algorithms, the proposed one can achieve better scheduling performance considering the urgency criterion, the importance criterion and the desired execution time criterion comprehensively. The average running time shows the proposed algorithm has real-time performance.

How multi-unmanned aerial vehicles (UAVs) carrying a payload pass an obstacle-dense environment is practically important. Up to now, there have been few results on safe motion planning for the multi-UAVs cooperative transportation system (CTS) to pass through such an environment. The problem is challenging because it is difficult to analyze and explicitly take into account the swing motion of the payload in planning. In this paper, a modeling method of virtual tube is proposed by fusing the advantages of the existing modeling algorithm for regular virtual tube and the expansion environment method. The proposed method can not only generate a safe and smooth tube for UAVs, but also ensure the payload stays away from the dense obstacles. Simulation results show the effectiveness of the method and the safety of the planned tube.

This paper presents a method of multicopter interception control based on visual servo and virtual tube in a cluttered environment. The proposed hybrid heuristic function improves the efficiency of the A* algorithm. The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A* algorithm. In six different simulation scenarios, the efficiency of the modified A* algorithm is 6.2% higher than that of the traditional A* algorithm. The efficiency of path planning and virtual tube planning is verified by simulations. The effectiveness of interception control is verified by a software-in-loop (SIL) simulation.

Unmanned aerial vehicles (UAVs) have become one of the key technologies to achieve future data collection due to their high mobility, rapid deployment, low cost, and the ability to establish line-of-sight communication links. However, when UAV swarm perform tasks in narrow spaces, they often encounter various spatial obstacles, building shielding materials, and high-speed node movements, which result in intermittent network communication links and cannot support the smooth completion of tasks. In this paper, a high mobility and dynamic topology of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering (HDMTC) algorithm is proposed. Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of network, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for UAV swarm networks.

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.

Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.

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.

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.

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.

To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition. This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat. This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment. Simulation results demonstrate that, compared to classical intention recognition models, the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.

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.

In this paper, the newly-derived maximum correntropy Kalman filter (MCKF) is re-derived from the M-estimation perspective, where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions. Based on the derivation process, a unified form for the robust Gaussian filters (RGF) based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement. The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals. Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust anti-jamming performance.

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.