Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobil...Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.展开更多
With the rapid development of informatization,autonomy and intelligence,unmanned swarm formation intelligent operations will become the main combat mode of future wars.Typical unmanned swarm formations such as ground-...With the rapid development of informatization,autonomy and intelligence,unmanned swarm formation intelligent operations will become the main combat mode of future wars.Typical unmanned swarm formations such as ground-based directed energy weapon formations,space-based kinetic energy weapon formations,and sea-based carrier-based formations have become the trump card for winning future wars.In a complex confrontation environment,these sophisticated weapon formation systems can precisely strike mobile threat group targets,making them extreme deterrents in joint combat applications.Based on this,first,this paper provides a comprehensive summary of the outstanding advantages,strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare.Second,a detailed analysis of the technological breakthroughs in four key areas,situational awareness,heterogeneous coordination,mixed combat,and intelligent assessment of typical unmanned aerial vehicle(UAV)swarms in joint warfare,is presented.An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control.Then,an indepth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control.Finally,the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.展开更多
基金Project(2013AA06A411)supported by the National High Technology Research and Development Program of ChinaProject(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
基金the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0075,2022JM-395).
文摘With the rapid development of informatization,autonomy and intelligence,unmanned swarm formation intelligent operations will become the main combat mode of future wars.Typical unmanned swarm formations such as ground-based directed energy weapon formations,space-based kinetic energy weapon formations,and sea-based carrier-based formations have become the trump card for winning future wars.In a complex confrontation environment,these sophisticated weapon formation systems can precisely strike mobile threat group targets,making them extreme deterrents in joint combat applications.Based on this,first,this paper provides a comprehensive summary of the outstanding advantages,strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare.Second,a detailed analysis of the technological breakthroughs in four key areas,situational awareness,heterogeneous coordination,mixed combat,and intelligent assessment of typical unmanned aerial vehicle(UAV)swarms in joint warfare,is presented.An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control.Then,an indepth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control.Finally,the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.