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Two-dimensional directional modulation with dual-mode vortex beam for security transmission
Changju ZHU, Maozhong SONG, Xiaoyu DANG, Qiuming ZHU
Journal of Systems Engineering and Electronics    2022, 33 (6): 1108-1118.   DOI: 10.23919/JSEE.2022.000136
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A two-dimensional directional modulation (DM) technology with dual-mode orbital angular momentum (OAM) beam is proposed for physical-layer security of the relay unmanned aerial vehicle (UAV) tracking transmission. The elevation and azimuth of the vortex beam are modulated into the constellation, which can form the digital waveform with the encoding modulation. Since the signal is direction-dependent, the modulated waveform is purposely distorted in other directions to offer a security technology. Two concentric uniform circular arrays (UCAs) with different radii are excited to generate dual vortex beams with orthogonality for the composite signal, which can increase the demodulation difficulty. Due to the phase propagation characteristics of vortex beam, the constellation at the desired azimuth angle will change continuously within a wavelength. A desired single antenna receiver can use the propagation phase compensation and an opposite helical phase factor for the signal demodulation in the desired direction. Simulations show that the proposed OAM-DM scheme offers a security approach with direction sensitivity transmission.

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A deep reinforcement learning method for multi-stage equipment development planning in uncertain environments
Peng LIU, Boyuan XIA, Zhiwei YANG, Jichao LI, Yuejin TAN
Journal of Systems Engineering and Electronics    2022, 33 (6): 1159-1175.   DOI: 10.23919/JSEE.2022.000140
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Equipment development planning (EDP) is usually a long-term process often performed in an environment with high uncertainty. The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations. To deal with this problem, a multi-stage EDP model based on a deep reinforcement learning (DRL) algorithm is proposed to respond quickly to any environmental changes within a reasonable range. Firstly, the basic problem of multi-stage EDP is described, and a mathematical planning model is constructed. Then, for two kinds of uncertainties (future capability requirements and the amount of investment in each stage), a corresponding DRL framework is designed to define the environment, state, action, and reward function for multi-stage EDP. After that, the dueling deep Q-network (Dueling DQN) algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme. Finally, a case of ten kinds of equipment in 100 possible environments, which are randomly generated, is used to test the feasibility and effectiveness of the proposed models. The results show that the algorithm can respond instantaneously in any state of the multi-stage EDP environment and unlike traditional algorithms, the algorithm does not need to re-optimize the problem for any change in the environment. In addition, the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.

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Adaptive resource allocation for workflow containerization on Kubernetes
Chenggang SHAN, Chuge WU, Yuanqing XIA, Zehua GUO, Danyang LIU, Jinhui ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (3): 723-743.   DOI: 10.23919/JSEE.2023.000073
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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.

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Threat sequencing of multiple UCAVs with incomplete information based on game theory
Shouyi LI, Mou CHEN, Qingxian WU, Yuhui WANG
Journal of Systems Engineering and Electronics    2022, 33 (4): 986-996.   DOI: 10.23919/JSEE.2022.000095
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The threat sequencing of multiple unmanned combat air vehicles (UCAVs) is a multi-attribute decision-making (MADM) problem. In the threat sequencing process of multiple UCAVs, due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker ( $\mathcal{D}$ ) and nature ( $\mathcal{N}$ ) with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then, the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs (matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.

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Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning
Lei HU, Guoxing YI, Yi NAN, Hao WANG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1191-1210.   DOI: 10.23919/JSEE.2023.000119
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Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.

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Knowledge transfer in multi-agent reinforcement learning with incremental number of agents
Wenzhang LIU, Lu DONG, Jian LIU, Changyin SUN
Journal of Systems Engineering and Electronics    2022, 33 (2): 447-460.   DOI: 10.23919/JSEE.2022.000045
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In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others, which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.

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An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture
Renjie XU, Xin LIU, Donghao CUI, Jian XIE, Lin GONG
Journal of Systems Engineering and Electronics    2023, 34 (3): 574-587.   DOI: 10.23919/JSEE.2023.000081
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The contribution rate of equipment system-of-systems architecture (ESoSA) is an important index to evaluate the equipment update, development, and architecture optimization. Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems (ESoS), and the Bayesian network is an effective tool to solve the uncertain information, a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network (FBN) is proposed. Firstly, based on the operation loop theory, an ESoSA is constructed considering three aspects: reconnaissance equipment, decision equipment, and strike equipment. Next, the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information. Furthermore, the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA, and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established. Finally, the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA. Compared with traditional methods, the evaluation method based on FBN takes various failure states of equipment into consideration, is free of acquiring accurate probability of traditional equipment failure, and models the uncertainty of the relationship between equipment. The proposed method not only supplements and improves the ESoSA contribution rate assessment method, but also broadens the application scope of the Bayesian network.

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Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images
Hao FENG, Jianzhong WU, Lu ZHANG, Mingsheng LIAO
Journal of Systems Engineering and Electronics    2022, 33 (4): 896-906.   DOI: 10.23919/JSEE.2022.000087
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Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

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Navigation jamming signal recognition based on long short-term memory neural networks
Dong FU, Xiangjun LI, Weihua MOU, Ming MA, Gang OU
Journal of Systems Engineering and Electronics    2022, 33 (4): 835-844.   DOI: 10.23919/JSEE.2022.000083
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This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).

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Super-resolution DOA estimation for correlated off-grid signals via deep estimator
Shuang WU, Ye YUAN, Weike ZHANG, Naichang YUAN
Journal of Systems Engineering and Electronics    2022, 33 (6): 1096-1107.   DOI: 10.21629/JSEE.2022.00074
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This paper develops a deep estimator framework of deep convolution networks (DCNs) for super-resolution direction of arrival (DOA) estimation. In addition to the scenario of correlated signals, the quantization errors of the DCN are the major challenge. In our deep estimator framework, one DCN is used for spectrum estimation with quantization errors, and the remaining two DCNs are used to estimate quantization errors. We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation. Then, we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate. Also, the feasibility of the proposed deep estimator is analyzed in detail in this paper. Once the deep estimator is appropriately trained, it can recover the correlated signals’ spatial spectrum fast and accurately. Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

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Design and implementation of data-driven predictive cloud control system
Runze GAO, Yuanqing XIA, Li DAI, Zhongqi SUN, Yufeng ZHAN
Journal of Systems Engineering and Electronics    2022, 33 (6): 1258-1268.   DOI: 10.23919/JSEE.2022.000146
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The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems. Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources. However, the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized. Meanwhile, it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms. Therefore, motivated by the above reasons, we propose a data-driven predictive cloud control system. To achieve the proposed system, a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed. Finally, the simulations and experiments demonstrate the effectiveness of the proposed system.

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Torque estimation for robotic joint with harmonic drive transmission based on system dynamic characteristics
Minghong ZHU, Shu XIAO, Fei YU
Journal of Systems Engineering and Electronics    2022, 33 (6): 1320-1331.   DOI: 10.23919/JSEE.2022.000151
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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.

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Classification of birds and drones by exploiting periodical motions in Doppler spectrum series
Jia DUAN, Lei ZHANG, Yifeng WU, Yue ZHANG, Zeya ZHAO, Xinrong GUO
Journal of Systems Engineering and Electronics    2023, 34 (1): 19-27.   DOI: 10.23919/JSEE.2023.000002
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With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections (RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from high-resolution Doppler spectrum sequences (DSSs) for classification. This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory (LSTM) is used to solve the time series classification. Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

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Product quality prediction based on RBF optimized by firefly algorithm
Huihui HAN, Jian WANG, Sen CHEN, Manting YAN
Journal of Systems Engineering and Electronics    2024, 35 (1): 105-117.   DOI: 10.23919/JSEE.2023.000061
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With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive experiments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous methods on predicting pro-duct quality.

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Classification of knowledge graph completeness measurement techniques
Ying ZHANG, Gang XIAO
Journal of Systems Engineering and Electronics    2024, 35 (1): 154-162.   DOI: 10.23919/JSEE.2023.000150
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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.

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A self-organization formation configuration based assignment probability and collision detection
Wei SONG, Tong WANG, Guangxin YANG, Peng ZHANG
Journal of Systems Engineering and Electronics    2024, 35 (1): 222-232.   DOI: 10.23919/JSEE.2024.000016
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The formation control of multiple unmanned aerial vehicles (multi-UAVs) has always been a research hotspot. Based on the straight line trajectory, a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption. In order to avoid the collision between UAVs in the formation process, the concept of safety ball is introduced, and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs. Based on the idea of game theory, a method of UAV motion form setting based on the maximization of interests is proposed, including the maximization of self-interest and the maximization of formation interest is proposed, so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance. Finally, through simulation verification, the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length, and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

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Robust least squares projection twin SVM and its sparse solution
Shuisheng ZHOU, Wenmeng ZHANG, Li CHEN, Mingliang XU
Journal of Systems Engineering and Electronics    2023, 34 (4): 827-838.   DOI: 10.23919/JSEE.2023.000103
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Least squares projection twin support vector machine (LSPTSVM) has faster computing speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is sensitive to outliers and its solution lacks sparsity. Therefore, it is difficult for LSPTSVM to process large-scale datasets with outliers. In this paper, we propose a robust LSPTSVM model (called R-LSPTSVM) by applying truncated least squares loss function. The robustness of R-LSPTSVM is proved from a weighted perspective. Furthermore, we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space. Finally, the sparse R-LSPTSVM algorithm (SR-LSPTSVM) is proposed. Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

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Autonomous landing scene recognition based on transfer learning for drones
Hao DU, Wei WANG, Xuerao WANG, Yuanda WANG
Journal of Systems Engineering and Electronics    2023, 34 (1): 28-35.   DOI: 10.23919/JSEE.2023.000031
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In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network (CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum (ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent (SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.8450% top-1 accuracy on the LandingScenes-7 dataset, paving the way for drones to autonomously learn landing scenes.

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Solving open vehicle problem with time window by hybrid column generation algorithm
Naikang YU, Bin QIAN, Rong HU, Yuwang CHEN, Ling WANG
Journal of Systems Engineering and Electronics    2022, 33 (4): 997-1009.   DOI: 10.23919/JSEE.2022.000096
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This paper addresses the open vehicle routing problem with time window (OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task. The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process. We propose a hybrid column generation algorithm (HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

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A multiple heterogeneous UAVs reconnaissance mission planning and re-planning algorithm
Lei HU, Boqi XI, Guoxing YI, Hui ZHAO, Jiapeng ZHONG
Journal of Systems Engineering and Electronics    2022, 33 (6): 1190-1207.   DOI: 10.23919/JSEE.2022.000142
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Reconnaissance mission planning of multiple unmanned aerial vehicles (UAVs) under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial (NP)-complete problem. The purpose of this study is to research intelligent multi-UAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas. For numerous targets scattered in the wide area, a reconnaissance mission planning and re-planning system is established, which includes five modules, including intelligence analysis, sub-mission area division, mission sequence planning, path smoothing, and online re-planning. The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints. In the sub-mission area division module, an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas, and each sub-mission is detected by the UAV loaded with various detective sensors. To control reconnaissance cost, the sampling and iteration algorithms are proposed in the mission sequence planning module, which are utilized to solve the optimal or approximately optimal reconnaissance sequence. In the path smoothing module, the Dubins curve is applied to smooth the flight path, which assure the availability of the planned path. Furthermore, an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged. Finally, reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

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A review of addressing class noise problems of remote sensing classification
Wei FENG, Yijun LONG, Shuo WANG, Yinghui QUAN
Journal of Systems Engineering and Electronics    2023, 34 (1): 36-46.   DOI: 10.23919/JSEE.2023.000034
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The development of image classification is one of the most important research topics in remote sensing. The prediction accuracy depends not only on the appropriate choice of the machine learning method but also on the quality of the training datasets. However, real-world data is not perfect and often suffers from noise. This paper gives an overview of noise filtering methods. Firstly, the types of noise and the consequences of class noise on machine learning are presented. Secondly, class noise handling methods at both the data level and the algorithm level are introduced. Then ensemble-based class noise handling methods including class noise removal, correction, and noise robust ensemble learners are presented. Finally, a summary of existing data-cleaning techniques is given.

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Hybrid TDOA/FDOA and track optimization of UAV swarm based on A-optimality
Hao LI, Hemin SUN, Ronghua ZHOU, Huainian ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (1): 149-159.   DOI: 10.23919/JSEE.2023.000008
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The source location based on the hybrid time difference of arrival (TDOA)/frequency difference of arrival (FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle (UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision (GDOP) factor. Second, the Cramer-Rao lower bound (CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.

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Deep convolutional neural network for meteorology target detection in airborne weather radar images
Chaopeng YU, Wei XIONG, Xiaoqing LI, Lei DONG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1147-1157.   DOI: 10.23919/JSEE.2023.000142
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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.

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Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR
Weikun HE, Jingbo SUN, Xinyun ZHANG, Zhenming LIU
Journal of Systems Engineering and Electronics    2022, 33 (6): 1127-1139.   DOI: 10.23919/JSEE.2022.000138
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Micro-Doppler feature extraction of unmanned aerial vehicles (UAVs) is important for their identification and classification. Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters. The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio (SNR). Then considering the rotor rate variance of UAV in the complex motion state, the cepstrum method is improved to extract the rotation rate of the UAV, and the blade length can be intensively estimated. The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved. However, the computation complexity is higher and the heavier computation burden is required.

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A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process
Jingfeng LI, Yunxiang CHEN, Zhongyi CAI, Zezhou WANG
Journal of Systems Engineering and Electronics    2022, 33 (2): 474-488.   DOI: 10.23919/JSEE.2022.000047
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An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance (PM) on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

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A self-adaptive grey forecasting model and its application
Xiaozhong TANG, Naiming XIE
Journal of Systems Engineering and Electronics    2022, 33 (3): 665-673.   DOI: 10.23919/JSEE.2022.000061
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GM(1,1) models have been widely used in various fields due to their high performance in time series prediction.However, some hypotheses of the existing GM(1,1) model family may reduce their prediction performance in some cases. To solve this problem, this paper proposes a self-adaptive GM(1,1) model, termed as SAGM(1,1) model, which aims to solve the defects of the existing GM (1,1) model family by deleting their modeling hypothesis. Moreover, a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed, the proposed multi-parameter optimization scheme adopts machine learning ideas, takes all adjustable parameters of SAGM(1,1) model as input variables, and trains it with firefly algorithm. And Sobol’ sensitivity indices are applied to study global sensitivity of SAGM(1,1) model parameters, which provides an important reference for model parameter calibration. Finally, forecasting capability of SAGM(1,1) model is illustrated by Anhui electricity consumption dataset. Results show that prediction accuracy of SAGM(1,1) model is significantly better than other models, and it is shown that the proposed approach enhances the prediction performance of GM(1,1) model significantly.

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Exact uncertainty compensation of linear systems by continuous fixed-time output-feedback controller
Shang SHI, Guosheng ZHANG, Huifang MIN, Yinlong HU, Yonghui SUN
Journal of Systems Engineering and Electronics    2022, 33 (3): 706-715.   DOI: 10.23919/JSEE.2022.000065
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This paper studies the fixed-time output-feedback control for a class of linear systems subject to matched uncertainties. To estimate the uncertainties and system states, we design a composite observer which consists of a high-order sliding mode observer and a Luenberger observer. Then, a robust output-feedback controller with fixed-time convergence guarantee is constructed. Rigorous theoretical proof shows that with the proposed controller, the system states can converge to zero in fixed-time free of the initial conditions. Finally, simulation comparison with existing algorithms is given. Simulation results verify the effectiveness of the proposed controller in terms of its fixed-time convergence and perfect disturbance rejection.

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Generalized degradation reliability model considering phase transition
ZHANG Ao, Zhihua WANG, Qiong WU, Chengrui LIU
Journal of Systems Engineering and Electronics    2022, 33 (3): 748-758.   DOI: 10.23919/JSEE.2022.000068
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Aiming to evaluate the reliability of phase-transition degrading systems, a generalized stochastic degradation model with phase transition is constructed, and the corresponding analytical reliability function is formulated under the concept of the first hitting time. The phase-varying stochastic property and the phase-varying nonlinearity are considered simultaneously in the proposed model. To capture the phase-varying stochastic property, a Wiener process is adopted to model the non-monotonous degradation phase, while a Gamma process is utilized to model the monotonous one. In addition, the phase-varying nonlinearity is captured by different transformed time scale functions. To facilitate the practical application of the proposed model, identification of phase model type and estimation of model parameters are discussed, and the initial guesses for parameters optimization are also given. Based on the constructed model, two simulation studies are carried out to verify the analytical reliability function and analyze the influence of model misspecification. Finally, a practical case study is conducted for illustration.

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Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data
Fengfei WANG, Shengjin TANG, Xiaoyan SUN, Liang LI, Chuanqiang YU, Xiaosheng SI
Journal of Systems Engineering and Electronics    2023, 34 (1): 247-258.   DOI: 10.23919/JSEE.2023.000006
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Remaining useful life (RUL) prediction is one of the most crucial elements in prognostics and health management (PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression (RCR) model with fusing failure time data. Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures, the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function (PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

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Network-based structure optimization method of the anti-aircraft system
Qingsong ZHAO, Junyi DING, Jichao LI, Huachao LI, Boyuan XIA
Journal of Systems Engineering and Electronics    2023, 34 (2): 374-395.   DOI: 10.23919/JSEE.2023.000019
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The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

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Deep reinforcement learning for UAV swarm rendezvous behavior
Yaozhong ZHANG, Yike LI, Zhuoran WU, Jialin XU
Journal of Systems Engineering and Electronics    2023, 34 (2): 360-373.   DOI: 10.23919/JSEE.2023.000056
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The unmanned aerial vehicle (UAV) swarm technology is one of the research hotspots in recent years. With the continuous improvement of autonomous intelligence of UAV, the swarm technology of UAV will become one of the main trends of UAV development in the future. This paper studies the behavior decision-making process of UAV swarm rendezvous task based on the double deep Q network (DDQN) algorithm. We design a guided reward function to effectively solve the problem of algorithm convergence caused by the sparse return problem in deep reinforcement learning (DRL) for the long period task. We also propose the concept of temporary storage area, optimizing the memory playback unit of the traditional DDQN algorithm, improving the convergence speed of the algorithm, and speeding up the training process of the algorithm. Different from traditional task environment, this paper establishes a continuous state-space task environment model to improve the authentication process of UAV task environment. Based on the DDQN algorithm, the collaborative tasks of UAV swarm in different task scenarios are trained. The experimental results validate that the DDQN algorithm is efficient in terms of training UAV swarm to complete the given collaborative tasks while meeting the requirements of UAV swarm for centralization and autonomy, and improving the intelligence of UAV swarm collaborative task execution. The simulation results show that after training, the proposed UAV swarm can carry out the rendezvous task well, and the success rate of the mission reaches 90%.

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Threshold-type memristor-based crossbar array design and its application in handwritten digit recognition
Qingjian LI, Yan LIANG, Zhenzhou LU, Guangyi WANG
Journal of Systems Engineering and Electronics    2023, 34 (2): 324-334.   DOI: 10.23919/JSEE.2023.000018
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Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the Von Neumann architecture. Inspired by the real characteristics of physical memristive devices, we propose a threshold-type nonlinear voltage-controlled memristor mathematical model which is used to design a novel memristor-based crossbar array. The presented crossbar array can simulate the synaptic weight in real number field rather than only positive number field. Theoretical analysis and simulation results of a 2×2 image inversion operation validate the feasibility of the proposed crossbar array and the necessary training and inference functions. Finally, the presented crossbar array is used to construct the neural network and then applied in the handwritten digit recognition. The Mixed National Institute of Standards and Technology (MNIST) database is adopted to train this neural network and it achieves a satisfactory accuracy.

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Component reallocation and system replacement maintenance based on availability and cost in series systems
Yuqiang FU, Xiaoyang MA
Journal of Systems Engineering and Electronics    2022, 33 (6): 1342-1353.   DOI: 10.23919/JSEE.2022.000153
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Component reallocation (CR) is receiving increasing attention in many engineering systems with functionally interchangeable and unbalanced degradation components. This paper studies a CR and system replacement maintenance policy of series repairable systems, which undergoes minimal repairs for each emergency failure of components, and considers constant downtime and cost of minimal repair, CR and system replacement. Two binary mixed integer nonlinear programming models are respectively established to determine the assignment of CR, and the uptime right before CR and system replacement with the objective of minimizing the system average maintenance cost and maximizing the system availability. Further, we derive the optimal uptime right before system replacement with maximization of the system availability, and then give the relationship between the system availability and the component failure rate. Finally, numerical examples show that the CR and system replacement maintenance policy can effectively reduce the system average maintenance cost and improve the system availability, and further give the sensitivity analysis and insights of the CR and system replacement maintenance policy.

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Sliding mode fault tolerant consensus control for multi-agent systems based on super-twisting observer
Pu YANG, Xukai HU, Zixin WANG, Zhiqing ZHANG
Journal of Systems Engineering and Electronics    2022, 33 (6): 1309-1319.   DOI: 10.23919/JSEE.2022.000150
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The fault-tolerant consensus problem for leader-following nonlinear multi-agent systems with actuator faults is mainly investigated. A new super-twisting sliding mode observer is constructed to estimate the velocity and undetectable fault information simultaneously. The time-varying gain is introduced to solve the initial error problem and peak value problem, which makes the observation more accurate and faster. Then, based on the estimated results, an improved sliding mode fault-tolerant consensus control algorithm is designed to compensate the actuator faults. The protocol can guarantee the finite-time consensus control of multi-agent systems and suppress chattering. Finally, the effectiveness and the superiority of the observer and control algorithm are proved by some simulation examples of the multi-aircraft system.

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Multi-objective optimization of operation loop recommendation for kill web
Kewei YANG, Boyuan XIA, Gang CHEN, Zhiwei YANG, Minghao LI
Journal of Systems Engineering and Electronics    2022, 33 (4): 969-985.   DOI: 10.23919/JSEE.2022.000094
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In order to improve our military’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., “web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives non-dominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.

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Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage
Haipeng LI, Dazheng FENG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1101-1115.   DOI: 10.23919/JSEE.2023.000064
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This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions. This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence. Based on these properties and considering location restrictions, it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars. To overcome the non-convexity of the model and the non-analytical nature of the objective function, an algorithm combining integer line programming and the cuckoo search algorithm (CSA) is proposed. The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost. Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

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Scale effect removal and range migration correction for hypersonic target coherent detection
Shang WU, Zhi SUN, Xingtao JIANG, Haonan ZHANG, Jiangyun DENG, Xiaolong LI, Guolong CUI
Journal of Systems Engineering and Electronics    2024, 35 (1): 14-23.   DOI: 10.23919/JSEE.2023.000151
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The detection of hypersonic targets usually confronts range migration (RM) issue before coherent integration (CI). The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condition. However, with the increasing requirement of far-range detection, the time bandwidth 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 CI 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.

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Design and pricing of maintenance service contract based on Nash non-cooperative game approach
Chun SU, Kui HUANG
Journal of Systems Engineering and Electronics    2024, 35 (1): 118-129.   DOI: 10.23919/JSEE.2024.000010
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Nowadays manufacturers are facing fierce challenge. Apart from the products, providing customers with multiple maintenance options in the service contract becomes more popular, since it can help to improve customer satisfaction, and ultimately promote sales and maximize profit for the manufacturer. By considering the combinations of corrective maintenance and preventive maintenance, totally three types of maintenance service contracts are designed. Moreover, attractive incentive and penalty mechanisms are adopted in the contracts. On this basis, Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers, and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation. Numerical experiments are conducted. The results show that by taking into account the incentive and penalty mechanisms, the revenue can be improved for both the customers and manufacturer. Moreover, with the increase of repair rate and improvement factor in the preventive maintenance, the revenue will increase gradually for both the parties.

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Accurately tracking hypersonic gliding vehicles via an LEO mega-constellation in relay tracking mode
Zhao LI, Yidi WANG, Wei ZHENG
Journal of Systems Engineering and Electronics    2024, 35 (1): 211-221.   DOI: 10.23919/JSEE.2023.000078
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In order to effectively defend against the threats of the hypersonic gliding vehicles (HGVs), HGVs should be tracked as early as possible, which is beyond the capability of the ground-based radars. Being benefited by the developing mega-constellations in low-Earth orbit, this paper proposes a relay tracking mode to track HGVs to overcome the above problem. The whole tracking mission is composed of several tracking intervals with the same duration. Within each tracking interval, several appropriate satellites are dispatched to track the HGV. Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion. The tracking performances of the proposed tracking mode and the other two traditional tracking modes, including the stare and track-rate modes, are compared by simulation. The results show that the relay tracking mode can track the whole trajectory of a HGV, while the stare mode can only provide a very short tracking arc. Moreover, the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.

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A multi-UAV deployment method for border patrolling based on Stackelberg game
Xing LEI, Xiaoxuan HU, Guoqiang WANG, He LUO
Journal of Systems Engineering and Electronics    2023, 34 (1): 99-116.   DOI: 10.23919/JSEE.2023.000022
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To strengthen border patrol measures, unmanned aerial vehicles (UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader, who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.

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