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.

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.

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.

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.

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.

Recently, the ontological metamodel plays an increasingly important role to specify systems in two forms: ontology and metamodel. Ontology is a descriptive model representing reality by a set of concepts, their interrelations, and constraints. On the other hand, metamodel is a more classical, but more powerful model in which concepts and relationships are represented in a prescriptive way. This study firstly clarifies the difference between the two approaches, then explains their advantages and limitations, and attempts to explore a general ontological metamodeling framework by integrating each characteristic, in order to implement semantic simulation model engineering. As a proof of concept, this paper takes the combat effectiveness simulation systems as a motivating case, uses the proposed framework to define a set of ontological composable modeling frameworks, and presents an underwater targets search scenario for running simulations and analyzing results. Finally, this paper expects that this framework will be generally used in other fields.

In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios, the limitations of existing research, including real-time calculation, accuracy efficiency trade-off, and the absence of the three-dimensional attack area model, restrict their practical applications. To address these issues, an improved backtracking algorithm is proposed to improve calculation efficiency. A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm. Furthermore, the age-layered population structure genetic programming (ALPS-GP ) algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area, considering real-time requirements. The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm. The study reveals a remarkable combination of high accuracy and efficient real-time computation, with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10^?4 s, thus meeting the requirements of real-time combat scenarios.

The rapid growth of mobile applications, the popularity of the Android system and its openness have attracted many hackers and even criminals, who are creating lots of Android malware. However, the current methods of Android malware detection need a lot of time in the feature engineering phase. Furthermore, these models have the defects of low detection rate, high complexity, and poor practicability, etc. We analyze the Android malware samples, and the distribution of malware and benign software in application programming interface (API) calls, permissions, and other attributes. We classify the software’s threat levels based on the correlation of features. Then, we propose deep neural networks and convolutional neural networks with ensemble learning (DCEL), a new classifier fusion model for Android malware detection. First, DCEL preprocesses the malware data to remove redundant data, and converts the one-dimensional data into a two-dimensional gray image. Then, the ensemble learning approach is used to combine the deep neural network with the convolutional neural network, and the final classification results are obtained by voting on the prediction of each single classifier. Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models, the proposed DCEL has a higher detection rate, higher recall rate, and lower computational cost.

In the field of model-based system assessment, mathematical models are used to interpret the system behaviors. However, the industrial systems in this intelligent era will be more manageable. Various management operations will be dynamically set, and the system will be no longer static as it is initially designed. Thus, the static model generated by the traditional model-based safety assessment (MBSA) approach cannot be used to accurately assess the dependability. There mainly exists three problems. Complex: huge and complex behaviors make the modeling to be trivial manual; Dynamic: though there are thousands of states and transitions, the previous model must be resubmitted to assess whenever new management arrives; Unreusable: as for different systems, the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same. Motivated by solving the above problems, this research studies a formal management specifying approach with the advantages of agility modeling, dynamic modeling, and specification design that can be re-suable. Finally, three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems. However, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to implement. This paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also investigated. Several experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DL-based prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.

The earth observation satellites (EOSs) scheduling problem for emergency tasks often presents many challenges. For example, the scheduling calculation should be completed in seconds, the scheduled task rate is supposed to be as high as possible, the disturbance measure of the scheme should be as low as possible, which may lead to the loss of important observation opportunities and data transmission delays. Existing scheduling algorithms are not designed for these requirements. Consequently, we propose a rolling horizon strategy (RHS) based on event triggering as well as a heuristic algorithm based on direct insertion, shifting, backtracking, deletion, and reinsertion (ISBDR). In the RHS, the driven scheduling mode based on the emergency task arrival and control station time window events are designed to transform the long-term, large-scale problem into a short-term, small-scale problem, which can improve the schedulability of the original scheduling scheme and emergency response sensiti-vity. In the ISBDR algorithm, the shifting rule with breadth search capability and backtracking rule with depth search capability are established to realize the rapid adjustment of the original plan and improve the overall benefit of the plan and early completion of emergency tasks. Simultaneously, two heuristic factors, namely the emergency task urgency degree and task conflict degree, are constructed to improve the emergency task scheduling guidance and algorithm efficiency. Finally, we conduct extensive experiments by means of simulations to compare the algorithms based on ISBDR and direct insertion, shifting, deletion, and reinsertion (ISDR). The results demonstrate that the proposed algorithm can improve the timeliness of emergency tasks and scheduling performance, and decrease the disturbance measure of the scheme, therefore, it is more suitable for emergency task scheduling.

The relationship between the technique by statedependent Riccati equations (SDRE) and Hamilton-Jacobi-Isaacs (HJI) equations for nonlinear H∞ control design is  investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equivalent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.

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.

Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Secondly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of
the advantages of M/K detector and integrating detector, a robust detector based on scatterer density (DBSD) is designed, which can reduce the probable collapsing loss or quantization error effectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indicate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD outperforms the existing scatterer-density-dependent detector.

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.

In a system of systems (SoS), resilience is an important factor in maintaining the functionality, stability, and enhancing the operation effectiveness. From the perspective of resilience, this paper studies the importance of the SoS, and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS. In this paper, the components of the SoS are simplified as four kinds of network nodes: sensor, decision point, influencer, and target. In this networked SoS, the number of operation loops is used as the performance indicator, and an approximate algorithm, which is based on eigenvalue of the adjacency matrix, is proposed to calculate the number of operation loops. In order to understand the performance change of the SoS during the attack and defense process in the operations, an integral resilience model is proposed to depict the resilience of the SoS. From different perspectives of enhancing the resilience, different measures, parameters and the corresponding algorithms for the resilience importance of components are proposed. Finally, a case study on an SoS is conducted to verify the validity of the network modelling and the resilience-based importance analysis method.

In this paper, a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor (SNRA-SSEMVS) is introduced, and a method for estimating 2D-direction of arrival (DOA) and polarization is devised. Firstly, according to the special structure of the sparse nonuniform rectangular array (SNRA), a set of accurate but ambiguous direction-cosine estimates can be obtained. Then the steering vector of spatially spread electromagnetic vector sensor (SSEMVS) can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates. Finally, the disambiguation approach can be used to get the final accurate estimates of 2D-DOA and polarization. Compared with some existing methods, the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas, as well as reduces the mutual coupling effect. Moreover, the proposed algorithm resolves multiple sources without the priori knowledge of signal information, suffers no ambiguity in the estimation of the Poynting vector, and pairs the x-axis direction cosine with the y-axis direction cosine automatically. Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

To deal with the problem that the block sparse Bayesian algorithm exists in grid estimation, an off-grid weighted block sparse Bayesian algorithm is proposed based on coherent accumulation. The algorithm first uses the signal characteristics to coherently accumulate the polarization-sensitive array received data to enhance the signal-to-noise ratio (SNR); then the first-order Taylor expansion of the steering vector is performed, and an off-grid real-valued model is introduced by improving the block structure; then the weighting vectors are introduced to accelerate the iteration of the algorithm and reduce the number of iterations; and finally, the solution of the off-grid parameters is achieved by iterative optimization of the parameters. Compared with the traditional block sparse Bayesian learning (BSBL) algorithm, the method iterates faster and achieves efficient joint off-grid polarization-DOA estimation. Simulation results show the effectiveness of the algorithm.

In the applications of joint control and robot movement, the joint torque estimation has been treated as an effective technique and widely used. Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output. Through analyzing the structures of the harmonic drive and experiment apparatus, a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter (UKF) is designed and built. Based on research and scheme, torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique. Finally, a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed, and simulation results compared with the measurements of a commercial torque sensor, have verified the effectiveness of the proposed method.

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed. The pre-registration is achieved by transforming the multi-pose models to the standard frontal model’s reference frame using the principal axis analysis algorithm. Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics. These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration. Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

This paper proposes a fast integral terminal sliding mode (ITSM) control method for a cascaded nonlinear dynamical system with mismatched uncertainties. Firstly, an integral terminal sliding mode surface is presented, which not only avoids the singularity in the traditional terminal sliding mode, but also addresses the mismatched problems in the nonlinear control system. Secondly, a new ITSM controller with finite convergence time based on the backstepping technique is derived for a cascaded nonlinear dynamical system with mismatched uncertainties. Thirdly, the convergence time of ITSM is analyzed, whose convergence speed is faster than those of two nonsingular terminal sliding modes. Finally, simulation results are presented in order to evaluate the effectiveness of ITSM control strategies for mismatched uncertainties.

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.

Taking the real part and the imaginary part of complex sound pressure of the sound field as features, a transfer learning model is constructed. Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network (CNN), the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem. The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method, realize the range estimation for the shallow source in the experiment, and compare the range estimation performance of the underwater target sound source of four methods: matched field processing (MFP), generalized regression neural network (GRNN), traditional CNN, and transfer learning. Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes, and the estimation performance is remarkably better than that of other methods.

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters, the accuracy and confidence of meteorology target detection are reduced. In this paper, a deep convolutional neural network (DCNN) is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input. For each weather radar image, the corresponding digital elevation model (DEM) image is extracted on basis of the radar antenna scanning parameters and plane position, and is further fed to the network as a supplement for ground clutter suppression. The features of actual meteorology targets are learned in each bottleneck module of the proposed network and convolved into deeper iterations in the forward propagation process. Then the network parameters are updated by the back propagation iteration of the training error. Experimental results on the real measured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors. Meanwhile, the network outputs are in good agreement with the expected meteorology detection results (labels). It is demonstrated that the proposed network would have a promising meteorology observation application with minimal effort on network variables or parameter changes.

Foot-mounted pedestrian navigation system (PNS) is a common solution to pedestrian navigation using micro-electro mechanical system (MEMS) inertial sensors. The inherent problems of inertial navigation system (INS) by the traditional algorithm, such as the accumulated errors and the lack of observation of heading and altitude information, have become obstacles to the application and development of the PNS. In this paper, we introduce a heuristic heading constraint method. First of all, according to the movement characteristics of human gait, we use the generalized likelihood ratio test (GLRT) detector and introduce a time threshold to classify the human gait, so that we can effectively identify the stationary state of the foot. In addition, based on zero velocity update (ZUPT) and zero angular rate update (ZARU), the cumulative error of the inertial measurement unit (IMU) is limited and corrected, and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian. After simulation and experiments with low-cost IMU, the method is proved to reduce the localization error of end-point to less than 1% of the total distance, and it has great value in application.

Unmanned air vehicles (UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately, this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learning-based heuristic, namely, population based adaptive large neighborhood search (P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size (20 targets) instances in much shorter time.

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.

Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking, three coupled dynamic models of state estimation based on the priori information between guidance variables and aerodynamics are presented. Firstly, the aerodynamic acceleration acting on the target is analyzed to reveal the essence of the target's motion. Then three coupled structures for modeling aerodynamic parameters are developed by different ideas: the spiral model with a harmonic oscillator, the bank model with trigonometric functions of the bank angle and the guide model with the changing rule of guidance variables. Meanwhile, the comparison discussion is concluded to show the novelty and advantage of these models. Finally, a performance assessment in different simulation cases is presented and detailed analysis is revealed. The results show that the proposed models perform excellent properties. Moreover, the guide model produces the best tracking performance and the bank model shows the second; however, the spiral model does not outperform the maneuvering reentry vehicle (MaRV) model markedly.

In the case of the given design variables and constraint functions, this paper is concerned with the rapid overall parameters design of trajectory, propulsion and aerodynamics for long-range ballistic missiles based on the index of the minimum take-off mass. In contrast to the traditional subsystem independent design, this paper adopts the research idea of the combination of the subsystem independent design and the multisystem integration design. Firstly, the trajectory, propulsion and aerodynamics of the subsystem are separately designed by the engineering design, including the design of the minimum energy trajectory, the computation of propulsion system parameters, and the calculation of aerodynamic coefficient and dynamic derivative of the missile by employing the software of missile DATCOM. Then, the uniform design method is used to simplify the constraint conditions and the design variables through the integration design, and the accurate design of the optimized variables would be accomplished by adopting the uniform particle swarm optimization (PSO) algorithm. Finally, the automation design software is written for the three-stage solid ballistic missile. The take-off mass of 29~850 kg is derived by the subsystem independent design, and 20 constraints are reduced by employing the uniform design on the basis of 29 design variables and 32 constraints, and the take-off mass is dropped by 1~850 kg by applying the combination of the uniform design and PSO. The simulation results demonstrate the effectiveness and feasibility of the proposed hybrid optimization technique.

To improve the positioning accuracy in GPS point positioning, the geometric dilution of precision (GDOP) including HDOP, VDOP, TDOP, PDOP is commonly considered. The properties of the DOP for the GPS satellite navigation system are studied and the coordinate system is improved in order to decrease the amount of variables. In the end, by simulation and discussing the results, the corresponding conclusions are presented.

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.

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.

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.

A methodology for automatically generating risk scenarios is presented. Its main idea is to let the system model “express itself” through simulation. This is achieved by having the simulation model driven by an elaborated simulation engine, which: (i) manipulates the generation of branch points, i.e. event occurrence times; (ii) employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point. In addition, a backtracking technique, as an extension, is implemented to recover some missed risk scenarios. A widely discussed dynamic reliability example (a holdup tank) is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output (MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound (PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization (MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search (ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

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.

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.

This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals (TDOA) measurements. The solution uses a piecewise loss function named as mixed Huber loss (MHL) proposed based on the classical Huber loss (HL) and its refined version. The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds. In practice, appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers. A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers. Simulations are included to examine the solution’s performance and compare it with several benchmarks. The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks. The solution is shown to be even robust to multiple measurement outliers. Furthermore, the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.