A mathematical model to determine the optimal production lot size for a deteriorating production system under an extended product inspection policy is developed. The last-K product inspection policy is considered so that the nonconforming items can be reduced, under which the last K products in a production lot are inspected and the nonconforming items from those inspected are reworked. Consider that the products produced towards the end of a production lot are more likely to be nonconforming, is proposed an extended product inspection policy for a deteriorating production system. That is, in a production lot, product inspections are performed among the middle K1 items and after inspections, all of the last K2 products are directly reworked without inspections. Our objective here is the joint optimization of the production lot size and the corresponding extended inspection policy such that the expected total cost per unit time is minimized. Since there is no closed form expression for our optimal policy, the existence for the optimal production inspection policy and an upper bound for the optimal lot size are obtained. Furthermore, an efficient solution procedure is provided to search for the optimal policy. Finally, numerical examples are given to illustrate the proposed model and indicate that the expected total cost per unit time of our product inspection model is less than that of the last-K inspection policy.

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and flexibility. However, beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal, satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-performance reception. Firstly, this paper analyzes the key issues of burst communication for traffic signals in beam hopping systems, and then compares and studies typical carrier synchronization algorithms for burst signals. Secondly, combining the requirements of beam-hopping communication systems for efficient burst and low signal-to-noise ratio reception of downlink signals in forward links, a decoding assisted bidirectional variable parameter iterative carrier synchronization technique is proposed, which introduces the idea of iterative processing into carrier synchronization. Aiming at the technical characteristics of communication signal carrier synchronization, a new technical approach of bidirectional variable parameter iteration is adopted, breaking through the traditional understanding that loop structures cannot adapt to low signal-to-noise ratio burst demodulation. Finally, combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems, the research and performance simulation are conducted. The results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals, achieve fast synchronization of low signal-to-noise ratio burst signals, and have the unique advantage of flexible and adjustable parameters.

The rapid growth in satellite and aerial remote sensing platforms has created a growing need for distributed remote sensing scene classification. Conventional centralized scene classification methods, which involve transmitting remote sensing data to a ground station for processing, encounter limitations in both transmission efficiency and data privacy. Federated learning (FL) has emerged as a promising approach by enabling terminals to collaboratively train models without exchanging raw data. However, the non-independent and identically distributed (Non-IID) nature of remote sensing data significantly impedes FL performance. To address these challenges, a federated framework with feature distillation (FD) (FedFD) is proposed for FL-based remote sensing scene classification. Specifically, FedFD facilitates collaborative training by aggregating model parameters from multiple terminals to the cloud, thereby optimizing a global model. To further alleviate the impact of Non-IID data, an innovative partial feature-sharing strategy based on FD is designed, which divides features into globally shared essential features and locally maintained supplementary features. Moreover, to cope with object and scene scale variation, the squeeze and excitation module and the pyramid pooling module are incorporated into the scene classification network to enhance multiscale feature extraction. Extensive experiments on the Northwestern Polytechical University Remote Sensing Image Scene Classification 45 (NWPU-RESISC45) dataset and University of California, Merced Land Use (UC-Merced) dataset, under varying numbers of terminals and Non-IID levels, validate the effectiveness and scalability of FedFD, and demonstrate its superior performance in FL-based remote sensing scene classification.

With the strong battlefield application environment of the next generation fighter, based on the design of distributed vehicle management system, a fault diagnosis and fault-tolerant control (FTC) method for wing surface damage is proposed in this paper. Aiming at three kinds of wing damage modes, this paper proposes a diagnosis method based on the fault decision tree and forms a fault decision tree for wing damage from the aspects of sample database construction, feature parameter extraction, and fault decision tree construction. Based on the fault diagnosis results, the longitudinal control law based on dynamic inverse and the lateral-directional robust control laws based on linear quadratic regulator (LQR) are proposed. From the simulation examples, the fault diagnosis algorithm based on the decision tree can complete the judgment of three wing surface damage modes within 2 ms, and the FTC law can make the fighter quickly return to a stable flight state after a short transient of 1 s, which achieves the fault-tolerant goal.

This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.

Space-based optical (SBO) space surveillance has attracted widespread interest in the last two decades due to its considerable value in space situation awareness (SSA). SBO observation strategy, which is related to the performance of space surveillance, is the top-level design in SSA missions reviewed. The recognized real programs about SBO SAA proposed by the institutions in the U.S., Canada, Europe, etc., are summarized firstly, from which an insight of the development trend of SBO SAA can be obtained. According to the aim of the SBO SSA, the missions can be divided into general surveillance and space object tracking. Thus, there are two major categories for SBO SSA strategies. Existing general surveillance strategies for observing low earth orbit (LEO) objects and beyond-LEO objects are summarized and compared in terms of coverage rate, revisit time, visibility period, and image processing. Then, the SBO space object tracking strategies, which has experienced from tracking an object with a single satellite to tracking an object with multiple satellites cooperatively, are also summarized. Finally, this paper looks into the development trend in the future and points out several problems that challenges the SBO SSA.

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’ information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named adaptive resource allocation scheme (ARAS) for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod’s lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in centrol processing unit (CPU) and memory resource usage rate.

Numerous works prove that existing neighbor-averaging graph neural networks (GNNs) cannot efficiently catch structure features, and many works show that injecting structure, distance, position, or spatial features can significantly improve the performance of GNNs, however, injecting high-level structure and distance into GNNs is an intuitive but untouched idea. This work sheds light on this issue and proposes a scheme to enhance graph attention networks (GATs) by encoding distance and hop-wise structure statistics. Firstly, the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node. Secondly, the derived structure information, distance information, and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors. Thirdly, the derived embedding vectors are fed into GATs, such as GAT and adaptive graph diffusion network (AGDN) to get the soft labels. Fourthly, the soft labels are fed into correct and smooth (C&S) to conduct label propagation and get final predictions. Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks (DHSEGATs) achieve a competitive result.

Complex systems widely exist in nature and human society. There are complex interactions between system elements in a complex system, and systems show complex features at the macro level, such as emergence, self-organization, uncertainty, and dynamics. These complex features make it difficult to understand the internal operation mechanism of complex systems. Networked modeling of complex systems is a favorable means of understanding complex systems. It not only represents complex interactions but also reflects essential attributes of complex systems. This paper summarizes the research progress of complex systems modeling and analysis from the perspective of network science, including networked modeling, vital node analysis, network invulnerability analysis, network disintegration analysis, resilience analysis, complex network link prediction, and the attacker-defender game in complex networks. In addition, this paper presents some points of view on the trend and focus of future research on network analysis of complex systems.

Beyond-visual-range (BVR) air combat threat assessment has attracted wide attention as the support of situation awareness and autonomous decision-making. However, the traditional threat assessment method is flawed in its failure to consider the intention and event of the target, resulting in inaccurate assessment results. In view of this, an integrated threat assessment method is proposed to address the existing problems, such as overly subjective determination of index weight and imbalance of situation. The process and characteristics of BVR air combat are analyzed to establish a threat assessment model in terms of target intention, event, situation, and capability. On this basis, a distributed weight-solving algorithm is proposed to determine index and attribute weight respectively. Then, variable weight and game theory are introduced to effectively deal with the situation imbalance and achieve the combination of subjective and objective. The performance of the model and algorithm is evaluated through multiple simulation experiments. The assessment results demonstrate the accuracy of the proposed method in BVR air combat, indicating its potential practical significance in real air combat scenarios.

It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks. Using the idea of clustering, after clustering tasks according to spatio-temporal attributes, the clustered groups are linked into task sub-chains according to similarity. Then, based on the correlation between clusters, the child chains are connected to form a task chain. Therefore, the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension. When a sudden task occurs, a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks. Through the above improvements, the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks. In order to reflect the efficiency and applicability of the algorithm, a task allocation model for the unmanned aerial vehicle (UAV) group is constructed, and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed. Task assignment has been constructed. The study uses the self-adjusting characteristics of the bee colony to achieve task allocation. Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus, we consider system safety as a control problem based on the system-theoretic accident model, the processes (STAMP) model and the system theoretic process analysis (STPA) technique to compensate the deficiency of traditional techniques. Meanwhile, system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly, we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

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.

In this paper, an importance sampling maximum likelihood (ISML) estimator for direction-of-arrival (DOA) of incoherently distributed (ID) sources is proposed. Starting from the maximum likelihood estimation description of the uniform linear array (ULA), a decoupled concentrated likelihood function (CLF) is presented. A new objective function based on CLF which can obtain a closed-form solution of global maximum is constructed according to Pincus theorem. To obtain the optimal value of the objective function which is a complex high-dimensional integral, we propose an importance sampling approach based on Monte Carlo random calculation. Next, an importance function is derived, which can simplify the problem of generating random vector from a high-dimensional probability density function (PDF) to generate random variable from a one-dimensional PDF. Compared with the existing maximum likelihood (ML) algorithms for DOA estimation of ID sources, the proposed algorithm does not require initial estimates, and its performance is closer to Cramer-Rao lower bound (CRLB). The proposed algorithm performs better than the existing methods when the interval between sources to be estimated is small and in low signal to noise ratio (SNR) scenarios.

Bird’s-eye-view (BEV) perception is a core technology for autonomous driving systems. However, existing solutions face the dilemma of high costs associated with multi-modal methods and limited performance of vision-only approaches. To address this issue, this paper proposes a framework named “a lightweight pure visual BEV perception method based on dual distillation of spatial-temporal knowledge”. This framework innovatively designs a lightweight vision-only student model based on ResNet, which leverages a dual distillation mechanism to learn from a powerful teacher model that integrates temporal information from both image and light detection and ranging (LiDAR) modalities. Specifically, we distill efficient multi-modal feature extraction and spatial fusion capabilities from the BEVFusion model, and distill advanced temporal information fusion and spatiotemporal attention mechanisms from the BEVFormer model. This dual distillation strategy enables the student model to achieve perception performance close to that of multi-modal models without relying on LiDAR. Experimental results on the nuScenes dataset demonstrate that the proposed model significantly outperforms classical vision-only algorithms, achieves comparable performance to current state-of-the-art vision-only methods on the nuScenes detection leaderboard in terms of both mean average precision (mAP) and the nuScenes detection score (NDS) metrics, and exhibits notable advantages in inference computational efficiency. Although the proposed dual-teacher paradigm incurs higher offline training costs compared to single-model approaches, it yields a streamlined and highly efficient student model suitable for resource-constrained real-time deployment. This provides an effective pathway toward low-cost, high-performance autonomous driving perception systems.

Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufacturing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heterogeneous models has brought great challenges to the modeling and simulation (M&S) science and technology. This paper presents a top-level modeling theory based on the meta modeling framework (M2F) of the COllaborative SIMulation (COSIM) theory of virtual prototyping to solve the problems. Firstly the fundamental principles of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special requirements in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the toplevel modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demonstrate the feasibility of the theory.

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues: the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combinationof finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system. Traditional importance measures mainly concerned the changing value of the system reliability caused by the change of the reliability of the component, and seldom considered the joint effect of the probability distribution, improvement rate of the object component. This paper studies the rate of the system reliability upgrading with an improvement of the component reliability for the multi-state consecutive k-out-of-n system. To verify the multi-state consecutive k-out-of-n system reliability upgrading by improving one component based on its improvement rate, an increasing potential importance (IPI) and its physical meaning are described at first. Secondly, the relationship between the IPI and Birnbaum importance measures are discussed. And the IPI for some different improvement actions of the component is further discussed. Thirdly, the characteristics of the IPI are analyzed. Finally, an application to an oil pipeline system is given.

Aimed at the abominable influences to blind equalization algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization (DFE) algorithm adapting to different underwater acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in different unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

The weapons system portfolio selection problem arises at the equipment demonstration stage and deals with the military application requirements. Further, the contribution rate of the system is one of the important indicators to evaluate the role of a system, which can facilitate the weapons system portfolio selection. Therefore, combining the system contribution rate with system portfolio selection is the focus of this study. It also focuses on calculating the contribution rates of multiple equipment systems with various types of capabilities. The contribution rate is measured by establishing a hierarchical multi-criteria value model from three dimensions. Based on the value model, the feasible portfolios are developed under certain cost constraints and the optimal weapons system portfolios are obtained by using the classification optimization selection strategy. Finally, an illustrative example is presented to verify the feasibility of the proposed model.

A schlieren detection algorithm is proposed for the ground-to-air background oriented schlieren (BOS) system to achieve high-speed airplane shock waves visualization. The proposed method consists of three steps. Firstly, image registration is incorporated for reducing errors caused by the camera motion. Then, the background subtraction dual-model single Gaussian model (BS-DSGM) is proposed to build a precise background model. The BS-DSGM could prevent the background model from being contaminated by the shock waves. Finally, the twodimensional orthogonal discrete wavelet transformation is used to extract schlieren information and averaging schlieren data. Experimental results show our proposed algorithm is able to detect the aircraft in-flight and to extract the schlieren information. The precision of schlieren detection algorithm is 0.96. Three image quality evaluation indices are chosen for quantitative analysis of the shock waves visualization. The white Gaussian noise is added in the frames to validate the robustness of the proposed algorithm. Moreover, we adopt two times and four times down sampling to simulate different imaging distances for revealing how the imaging distance affects the schlieren information in the BOS system.

Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images. Sub-look spectral analysis (SSA) is a common ambiguity-detection method, but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks. To overcome these specific limitations, this paper proposes an improved detection method. It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities. Non-negative matrix factorization is then employed to decompose this matrix, effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds. Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods.

KANG Rui was born in 1966. He is a Changjiang Scholar, and a distinguished professor awarded by the Chinese Ministry of Education. He is the Chinese director of International Center for Resilience and Safety of Critical Infrastructure (CRESCI) and the Chinese director of Sino-French Risk Science and Engineering (RISE) Lab. Now, he also works in School of Reliability and Systems Engineering, Beihang University, Beijing, China. He received his bachelor's and master's degrees in electrical engineering from Beihang University in 1987 and 1990, respectively. His main research interests include belief reliability theory, reliability-centered systems engineering, reliability design and testing theory and methods for high-reliable and long-lifetime product and resilience modeling and evaluation for cyber-physical system. He has published eight books and more than 150 research papers. He is also the associate editor of IEEE Transaction on Reliability and the associate editor of Proceedings of the Insititution of Mechanical Engineers, Part O: Journal of Risk and Reliability. He is the founder of China Prognostics and Health Management Society and also a famous reliability expert in Chinese industry. He received several awards from the Chinese government for his outstanding scientific contributions, including second prize of National Science and Technology Progress Award, and first prize for Science and Technology Progress Award of Ministry of Industry and Technology, etc. E-mail: kangrui@buaa.edu.cn

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.

The increasingly complex battlefield environment requests much closer connection in a team having both manned and unmanned aerial vehicles (MAVs and UAVs). This special heterogeneous team structure causes demands for effective organizational structure design solutions. Implementing adjustable autonomy in the organizational structure, the expected evaluation function is established based on the physical resource, intelligent resource, network efficiency, network vulnerability and task execution reliability. According to the above constraints, together with interaction latency, decision-making information processing capacity, and decision-making latency, we aim to find a preferential organizational structure. The proposed organizational structure includes cooperative relationships, supervisory control relationships, and decision-making authorization relationships. In addition, by considering the influence on the intelligent support capabilities and the task execution reliability created by adjustable autonomy, it helps to build the proposed organizational structure designed with certain degree of flexibility to deal with the potential changes in the unpredictable battlefield environment. Simulation is conducted to confirm our design to be valid. And the method is still valid under different battlefield environments and interventions.

The investigation of the effect of electrical and mechanical errors on the performance of a large active phased array antenna is studied. These errors can decrease the antenna performance, for instance, the gain reduction, side lobe level enhancement, and incorrect beam direction. In order to improve the performance of the antenna in the presence of these errors, phase error correction of large phased array antennas using the genetic algorithm (GA) is implemented. By using the phase compensation method, the antenna overall radiation pattern is recovered close to the ideal radiation pattern without error. By applying the simulation data to a 32×40 array of elements with a square grid at the frequency of S-band and measurement of the radiation pattern, the effectiveness of the proposed method is verified.

The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile. To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio (SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large, and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally, the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 dB. It can fully meet the requirement of 0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle (MAV/UAV) cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering (HFE). Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that, constraint conditions of intervention decision-making of commanders based on HFE (IDMCBHFE) are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search (VNS) algorithm, a K-best optimization variable neighborhood search mixed algorithm (KBOVNSMA) is designed to solve the model. Finally, through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Experimentsfrom both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier transform method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless

The function of the air target threat evaluation (TE) is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set (IFS) is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusioncomparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation, while its influencing factors are complex and mutually coupled. Existing calculation methods have very limited analysis of the influence mechanism of influencing factors, and none of them has analyzed the influence of the guidance law. This paper considers the influencing factors of both the interceptor and the target more comprehensively. Interceptor parameters include speed, guidance law, guidance error, fuze error, and fragment killing ability, while target performance includes speed, maneuverability, and vulnerability. In this paper, an interception model is established, Monte Carlo simulation is carried out, and the influence mechanism of each factor is analyzed based on the model and simulation results. Finally, this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors. The proposed method reduces the interference of invalid interception data to valid data, so its prediction accuracy is significantly better than that of pure regression neural networks.

The research and application of wireless local area networks (WLAN) technology are in a stage of rapid development. It has been one of research focuses of the wireless communications field. Through the use of enhanced single-user (SU)/multi-user (MU) multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) technology, the next generation WLAN IEEE 802.11ac dramatically increases the throughput. An improved MIMO-OFDM scheme based on modulation diversity is
proposed for the next generation WLAN. It uses two-dimensional modulation diversity to the current IEEE 802.11ac transmission scheme. Through the space-time-frequency component interleaver and the rotational modulation, the proposed scheme exhibits high spectral efficiency and low error rate in fading channels. The simulation results show that the proposed scheme significantly outperforms the SU/MU MIMO-OFDM scheme in the current IEEE 802.11ac standard, which is up to 5 dB.

The disturbance torque of aerostatic bearings is in the same order of the reaction wheel, which causes difficulty in evaluation of the designed attitude control strategy of a nano-satellite based on the aerostatic bearing. Two approaches are proposed to model the disturbance torque. Firstly, the gravity induced moment, the vortex torque, and the damping moment are modeled separately. However, the vortex torque and the damping moment are coupled with each other as both of them are caused by the viscosity. In the second approach, the coupling effect is considered. A nano-satellite is constructed based on aerostatic bearing. The time history of the free rotation rate from an initial speed is measured by the gyro, which is further used to calculate the rotation angle and acceleration. The static vortex torque is measured via the removable micro-torque measurement system. Based on these data, the model parameters are identified and modeling errors are presented. Results show that the second model is more precise. The root mean squire error is less than 0.5×10^-4 N·m and the relative error of the static vortex torque is 0.16%.

In multi-view image localization task, the features of the images captured from different views should be fused properly. This paper considers the classification-based image localization problem. We propose the relational graph location network (RGLN) to perform this task. In this network, we propose a heterogeneous graph construction approach for graph classification tasks, which aims to describe the location in a more appropriate way, thereby improving the expression ability of the location representation module. Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin. In addition, the proposed localization method outperforms the compared localization methods by around 1.7% in terms of meter-level accuracy.

The rapid evolution of unmanned aerial vehicle (UAV) technology and autonomous capabilities has positioned UAV as promising last-mile delivery means. Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode. Spatiotemporal collaboration, along with energy consumption with payload and wind conditions play important roles in delivery route planning. This paper introduces the traveling salesman problem with time window and onboard UAV (TSP-TWOUAV) and emphasizes the consideration of real-world scenarios, focusing on time collaboration and energy consumption with wind and payload. To address this, a mixed integer linear programming (MILP) model is formulated to minimize the energy consumption costs of vehicle and UAV. Furthermore, an adaptive large neighborhood search (ALNS) algorithm is applied to identify high-quality solutions efficiently. The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.