查看更多>>摘要：To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long short-term memory(RPP-LSTM)network is proposed,which com-bines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM net-works are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM net-work,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that com-pared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environ-ments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
查看更多>>摘要：The weapon and equipment operational requirement analysis(WEORA)is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge.The main challenge is that the existing weapons and equipment data fails to carry out struc-tured knowledge representation,and knowledge navigation based on natural language cannot efficiently support the WEORA.To solve above problem,this research proposes a method based on question answering(QA)of weapons and equipment knowledge graph(WEKG)to construct and navi-gate the knowledge related to weapons and equipment in the WEORA.This method firstly constructs the WEKG,and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation.Finally,the method is evaluated and a chatbot on the QA system is deve-loped for the WEORA.Our proposed method has good perfor-mance in the accuracy and efficiency of searching target knowl-edge,and can well assist the WEORA.
查看更多>>摘要：Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide.This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents,a classification model with light gradient boosting machine(LGBM)based on the aviation safety reporting system(ASRS)has been developed.It is improved by k-fold cross-validation with hybrid sampling model(HSCV),which may boost classification perfor-mance and maintain data balance.The results show that emp-loying the LGBM-HSCV model can significantly improve accu-racy while alleviating data imbalance.Vertical comparison with other cross-validation(CV)methods and lateral comparison with different fold times comprise the comparative approach.Aside from the comparison,two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order,and the other with more CV.Accord-ing to the assessment indices with different methods,the LGBM-HSCV model proposed here is effective at detecting incident causes.The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data pro-cessing,and the model's accurate identification of civil aviation incident causes can assist to improve civil aviation safety.
查看更多>>摘要：How to mine valuable information from massive multi-source heterogeneous data and identify the intention of aerial targets is a major research focus at present.Aiming at the long-term dependence of air target intention recognition,this paper deeply explores the potential attribute features from the spa-tiotemporal sequence data of the target.First,we build an intelli-gent dynamic intention recognition framework,including a series of specific processes such as data source,data preprocessing,target space-time,convolutional neural networks-bidirectional gated recurrent unit-atteneion(CBA)model and intention recog-nition.Then,we analyze and reason the designed CBA model in detail.Finally,through comparison and analysis with other recognition model experiments,our proposed method can effec-tively improve the accuracy of air target intention recognition,and is of significance to the commanders'operational command and situation prediction.
查看更多>>摘要：The rapid development of unmanned aerial vehicle(UAV)swarm,a new type of aerial threat target,has brought great pressure to the air defense early warning system.At present,most of the track correlation algorithms only use part of the target location,speed,and other information for correlation.In this paper,the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely,a route correlation method based on convolutional neural networks(CNN)and long short-term memory(LSTM)Neural network is designed.In this model,the CNN is used to extract the formation characteristics of UAV swarm and the spa-tial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm.Experimental results show that compared with the tradi-tional algorithms,the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target,and has better robustness and accuracy for swarm tar-gets.
查看更多>>摘要：The subversive nature of information war lies not only in the information itself,but also in the circulation and applica-tion of information.It has always been a challenge to quantita-tively analyze the function and effect of information flow through command,control,communications,computer,kill,intelligence,surveillance,reconnaissance(C4KISR)system.In this work,we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing,intelligence processing,decision making and fire attack.Specifically,the basic concept of force of information influence between nodes in C4KISR sys-tem is formally proposed and its mathematical definition is pro-vided.Then,based on the information entropy theory,the model of force of information influence between C4KISR system nodes is constructed.Finally,the simulation experiments have been performed under an air defense and attack scenario.The experi-mental results show that,with the proposed force of information influence framework,we can effectively evaluate the contribu-tion of information circulation through different C4KISR system nodes to the corresponding tasks.Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architec-ture.
查看更多>>摘要：An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipula-tors.The controller design incorporates prescribed performance functions(PPFs)to improve dynamic performance.Notably,the performance of the output error is confined in an envelope cha-racterized by exponential convergence,leading to convergence to zero.This feature ensures a prompt response from admit-tance control and establishes a reliable safety framework for interactions.Simulation results provide practical insights,demonstrating the viability of the control scheme proposed in this paper.
查看更多>>摘要：In this paper,a filtering method is presented to esti-mate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables.In this method,the long-short-term memory(LSTM)neural network is nested into the extended Kalman filter(EKF)to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties.To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the net-work output online.In the process of training the network,a multi-gradient descent learning mode is proposed to better fit the internal state of the system,and a rolling training is used to implement an online prediction logic.Based on the Lyapunov second method,we discuss the stability of the system,the result shows that when the training error of neural network is suffi-ciently small,the system is asymptotically stable.With its appli-cation to the estimation of time-varying parameters of a missile dual control system,the LSTM-EKF shows better filtering perfor-mance than the EKF and adaptive EKF(AEKF)when there exist large uncertainties in the system model.
查看更多>>摘要：Collaborative coverage path planning(CCPP)refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space.A multi-unmanned aerial vehicle(UAV)collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs,it can be considered that all UAVs fly at the same altitude,that is,they perform search tasks on a two-dimensional plane.Based on the agents'motion characteristics and environmental information,a mathematical model of CCPP problem is established.The minimum time for UAVs to complete the CCPP is the objective function,and com-plete coverage constraint,no-fly constraint,collision avoidance constraint,and communication constraint are considered.Four motion strategies and two communication strategies are designed.Then a distributed CCPP algorithm is designed based on hybrid strategies.Simulation results compared with pattern-based genetic algorithm(PBGA)and random search method show that the proposed method has stronger real-time perfor-mance and better scalability and can complete the complete CCPP task more efficiently and stably.
SHI GuoqingZHANG BoyanZHANG JiandongYANG Qiming...
473-484页
查看更多>>摘要：Aiming at the shortcoming that the traditional indus-trial manipulator using off-line programming cannot change along with the change of external environment,the key technolo-gies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the three-dimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adap-tive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking sys-tem is built by using the position-based visual servo.The simula-tion experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application require-ments of the manipulator tracking system in the practical experi-ments.
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