Pub Date : 2024-01-24DOI: 10.23919/jsee.2023.000161
Yanan Wang, Chaowei Zhou, Aifang Liu, Qin Mao
Three-dimensional (3D) synthetic aperture radar (SAR) extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry (InSAR) and SAR tomography (TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation (AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of 360°, which is similar to the flight path of airborne circular SAR (CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.
{"title":"SAR Regional All-Azimuth Observation Orbit Design for Target 3D Reconstruction","authors":"Yanan Wang, Chaowei Zhou, Aifang Liu, Qin Mao","doi":"10.23919/jsee.2023.000161","DOIUrl":"https://doi.org/10.23919/jsee.2023.000161","url":null,"abstract":"Three-dimensional (3D) synthetic aperture radar (SAR) extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry (InSAR) and SAR tomography (TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation (AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of 360°, which is similar to the flight path of airborne circular SAR (CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"22 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.23919/jsee.2023.000117
Luyi Yang, Haiyang Li, Jin Zhang, Yuehe Zhu
It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit's inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01° on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
{"title":"Fast Solution to the Free Return Orbit's Reachable Domain of the Manned Lunar Mission by Deep Neural Network","authors":"Luyi Yang, Haiyang Li, Jin Zhang, Yuehe Zhu","doi":"10.23919/jsee.2023.000117","DOIUrl":"https://doi.org/10.23919/jsee.2023.000117","url":null,"abstract":"It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit's inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01° on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.23919/jsee.2024.000007
Licong Zhang, Chunlin Gong, Hua Su, Da Ronch Andrea
The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MOO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MOO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.
微型导弹（MMs）的设计面临着一些新的挑战。任务要求以一定的精度到达目标，这就对制导精度提出了严格的要求。微型导弹的小型化使得制导、导航和控制（GNC）设计对主体设计（外形、电机和布局设计）及其设计目标（即飞行性能）的影响比以往更大。为了在飞行性能和制导精度之间进行权衡，在设计主体和 GNC 系统时必须考虑到所有相关的相互作用。在此，提出了一种多目标、多学科优化设计（MOO）方法。与电机、空气动力学、布局、轨迹、飞行动力学、控制和制导相关的学科都被纳入了所提出的 MOO 框架。优化问题旨在实现航程最大化和制导误差最小化。该问题采用非支配排序遗传算法 II 解决。最终获得了兼顾较远航程和较小制导误差的最优设计。最后，通过将最佳设计与传统方法提供的设计进行比较，总结了 MM 设计的经验教训，并深入探讨了飞行性能与制导精度之间的权衡。
{"title":"Design Methodology of a Mini-Missile Considering Flight Performance and Guidance Precision","authors":"Licong Zhang, Chunlin Gong, Hua Su, Da Ronch Andrea","doi":"10.23919/jsee.2024.000007","DOIUrl":"https://doi.org/10.23919/jsee.2024.000007","url":null,"abstract":"The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MOO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MOO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"85 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.23919/jsee.2024.000008
Yuqi Yuan, Di Zhou, Junlong Li, Chaofei Lou
In this paper, a filtering method is presented to estimate 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 network 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 sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.
{"title":"Time-Varying Parameters Estimation with Adaptive Neural Network EKF for Missile-Dual Control System","authors":"Yuqi Yuan, Di Zhou, Junlong Li, Chaofei Lou","doi":"10.23919/jsee.2024.000008","DOIUrl":"https://doi.org/10.23919/jsee.2024.000008","url":null,"abstract":"In this paper, a filtering method is presented to estimate 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 network 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 sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"68 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The detection of hypersonic targets usually confronts range migration (RM) issue before coherent integration (Cl). The traditional methods aiming at correcting RM to obtain Cl mainly considers the narrow-band radar condition. However, with the increasing requirement of far-range detection, the time band-width product, which is corresponding to radar's mean power, should be promoted in actual application. Thus, the echo signal generates the scale effect (SE) at large time bandwidth product situation, influencing the intra and inter pulse integration performance. To eliminate SE and correct RM, this paper proposes an effective algorithm, i.e., scaled location rotation transform (ScLRT). The ScLRT can remove SE to obtain the matching pulse compression (PC) as well as correct RM to complete Cl via the location rotation transform, being implemented by seeking the actual rotation angle. Compared to the traditional coherent detection algorithms, ScLRT can address the SE problem to achieve better detection/estimation capabilities. At last, this paper gives several simulations to assess the viability of ScLRT.
对高超声速目标的探测通常会在相干积分（Cl）之前遇到测距迁移（RM）问题。传统的方法主要考虑窄带雷达条件，旨在修正RM以获得Cl。然而，随着远距离探测要求的不断提高，在实际应用中应推广与雷达平均功率相对应的时间带宽乘积。因此，在大时间带宽积情况下，回波信号会产生尺度效应（SE），影响脉冲内和脉冲间的积分性能。为了消除 SE 并修正 RM，本文提出了一种有效的算法，即缩放位置旋转变换（ScLRT）。ScLRT 可通过位置旋转变换消除 SE 以获得匹配脉冲压缩（PC），并通过寻求实际旋转角度来修正 RM 以完成 Cl。与传统的相干检测算法相比，ScLRT 可以解决 SE 问题，从而获得更好的检测/估计能力。最后，本文给出了几个仿真来评估 ScLRT 的可行性。
{"title":"Scale Effect Removal and Range Migration Correction for Hypersonic Target Coherent Detection","authors":"Shang Wu, Zhi Sun, Xingtao Jiang, Haonan Zhang, Jiangyun Deng, Xiaolong Li, Guolong Cui","doi":"10.23919/jsee.2023.000151","DOIUrl":"https://doi.org/10.23919/jsee.2023.000151","url":null,"abstract":"The detection of hypersonic targets usually confronts range migration (RM) issue before coherent integration (Cl). The traditional methods aiming at correcting RM to obtain Cl mainly considers the narrow-band radar condition. However, with the increasing requirement of far-range detection, the time band-width product, which is corresponding to radar's mean power, should be promoted in actual application. Thus, the echo signal generates the scale effect (SE) at large time bandwidth product situation, influencing the intra and inter pulse integration performance. To eliminate SE and correct RM, this paper proposes an effective algorithm, i.e., scaled location rotation transform (ScLRT). The ScLRT can remove SE to obtain the matching pulse compression (PC) as well as correct RM to complete Cl via the location rotation transform, being implemented by seeking the actual rotation angle. Compared to the traditional coherent detection algorithms, ScLRT can address the SE problem to achieve better detection/estimation capabilities. At last, this paper gives several simulations to assess the viability of ScLRT.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"54 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-26DOI: 10.23919/jsee.2023.000136
Xueling Yang, Gong Zhang, Hu Song
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
{"title":"Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method","authors":"Xueling Yang, Gong Zhang, Hu Song","doi":"10.23919/jsee.2023.000136","DOIUrl":"https://doi.org/10.23919/jsee.2023.000136","url":null,"abstract":"In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.23919/jsee.2023.000159
Xinwei Ou, Zhangxin Chen, Ce Zhu, Yipeng Liu
Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are compressed by low rank approximation of the network parameters, which directly reduces the storage requirement with a smaller number of network parameters. In the time domain, the network parameters can be trained in a few subspaces, which enables efficient training for fast convergence. The model compression in the spatial domain is summarized into three categories as pre-train, pre-set, and compression-aware methods, respectively. With a series of integrable techniques discussed, such as sparse pruning, quantization, and entropy coding, we can ensemble them in an integration framework with lower computational complexity and storage. In addition to summary of recent technical advances, we have two findings for motivating future works. One is that the effective rank, derived from the Shannon entropy of the normalized singular values, outperforms other conventional sparse measures such as the $ell_{1}$ norm for network compression. The other is a spatial and temporal balance for tensorized neural networks. For accelerating the training of tensorized neural networks, it is crucial to leverage redundancy for both model compression and subspace training.
{"title":"Low Rank Optimization for Efficient Deep Learning: Making a Balance Between Compact Architecture And Fast Training","authors":"Xinwei Ou, Zhangxin Chen, Ce Zhu, Yipeng Liu","doi":"10.23919/jsee.2023.000159","DOIUrl":"https://doi.org/10.23919/jsee.2023.000159","url":null,"abstract":"Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are compressed by low rank approximation of the network parameters, which directly reduces the storage requirement with a smaller number of network parameters. In the time domain, the network parameters can be trained in a few subspaces, which enables efficient training for fast convergence. The model compression in the spatial domain is summarized into three categories as pre-train, pre-set, and compression-aware methods, respectively. With a series of integrable techniques discussed, such as sparse pruning, quantization, and entropy coding, we can ensemble them in an integration framework with lower computational complexity and storage. In addition to summary of recent technical advances, we have two findings for motivating future works. One is that the effective rank, derived from the Shannon entropy of the normalized singular values, outperforms other conventional sparse measures such as the <tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$ell_{1}$</tex> norm for network compression. The other is a spatial and temporal balance for tensorized neural networks. For accelerating the training of tensorized neural networks, it is crucial to leverage redundancy for both model compression and subspace training.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"8 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.23919/jsee.2023.000150
Ying Zhang, Gang Xiao
At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.
{"title":"Classification of Knowledge Graph Completeness Measurement Techniques","authors":"Ying Zhang, Gang Xiao","doi":"10.23919/jsee.2023.000150","DOIUrl":"https://doi.org/10.23919/jsee.2023.000150","url":null,"abstract":"At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"28 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.23919/JSEE.2023.000162
Jialong Zhang;Kun Han;Pu Zhang;Zhongxi Hou;Lei Ye
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 in-depth 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.
{"title":"A Survey on Joint-Operation Application for Unmanned Swarm Formations Under a Complex Confrontation Environment","authors":"Jialong Zhang;Kun Han;Pu Zhang;Zhongxi Hou;Lei Ye","doi":"10.23919/JSEE.2023.000162","DOIUrl":"https://doi.org/10.23919/JSEE.2023.000162","url":null,"abstract":"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 in-depth 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.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"34 6","pages":"1432-1446"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139488157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.23919/JSEE.2023.000153
Boyu Qin;Dong Zhang;Shuo Tang;Yang Xu
This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs' actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors' information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV's reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system's robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.
{"title":"Two-Layer Formation-Containment Fault-Tolerant Control of Fixed-Wing UAV Swarm for Dynamic Target Tracking","authors":"Boyu Qin;Dong Zhang;Shuo Tang;Yang Xu","doi":"10.23919/JSEE.2023.000153","DOIUrl":"https://doi.org/10.23919/JSEE.2023.000153","url":null,"abstract":"This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs' actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors' information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV's reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system's robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.","PeriodicalId":50030,"journal":{"name":"Journal of Systems Engineering and Electronics","volume":"34 6","pages":"1375-1396"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139488177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：