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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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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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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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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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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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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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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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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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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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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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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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Optimization of extended warranty cost for multi-component systems with economic dependence based on group maintenance
Rongcai WANG, Enzhi DONG, Zhonghua CHENG, Qian WANG
Journal of Systems Engineering and Electronics    2023, 34 (2): 396-407.   DOI: 10.23919/JSEE.2023.000009
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During extended warranty (EW) period, maintenance events play a key role in controlling the product systems within normal operations. However, the modelling of failure process and maintenance optimization is complicated owing to the complex features of the product system, namely, components of the multi-component system are interdependent with each other in some form. For the purpose of optimizing the EW pricing decision of the multi-component system scientifically and rationally, taking the series multi-component system with economic dependence sold with EW policy as a research object, this paper optimizes the imperfect preventive maintenance (PM) strategy from the standpoint of EW cost. Taking into consideration adjusting the PM moments of the components in the system, a group maintenance model is developed, in which the system is repaired preventively in accordance with a specified PM base interval. In order to compare with the system EW cost before group maintenance, the system EW cost model before group maintenance is developed. Numerical example demonstrates that offering group maintenance programs can reduce EW cost of the system to a great extent, thereby reducing the EW price, which proves to be a win-win strategy to manufacturers and users.

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Robust dual-channel correlation algorithm for complex weak target detection with wideband radar
Yan DAI, Dan LIU, Chuanming LI, Shaopeng WEI, Qingrong HU
Journal of Systems Engineering and Electronics    2023, 34 (5): 1130-1146.   DOI: 10.23919/JSEE.2023.000138
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In the scene of wideband radar, due to the spread of target scattering points, the attitude and angle of view of the target constantly change in the process of moving. It is difficult to predict, and the actual echo of multiple scattered points is not fully matched with the transmitted signal. Therefore, it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection. In addition, the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions. Therefore, this paper proposes a wideband target detection method based on dual-channel correlation processing of range-extended targets. This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself. The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal. The accumulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection. Finally, electromagnetic simulation experiments and measured data verify that the proposed method has the advantages of high signal to noise ratio (SNR) gain and high detection probability under low SNR conditions.

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FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems
Yuyuan ZHANG, Wenjun YAN, Limin ZHANG, Qing LING
Journal of Systems Engineering and Electronics    2023, 34 (2): 307-323.   DOI: 10.23919/JSEE.2023.000027
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The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.

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Dimension decomposition algorithm for multiple source localization using uniform circular array
Xiaolong SU, Panhe HU, Zhenhua WEI, Zhen LIU, Junpeng SHI, Xiang LI
Journal of Systems Engineering and Electronics    2023, 34 (3): 650-660.   DOI: 10.23919/JSEE.2023.000016
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A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that the far-field signal can be considered as the state where the range parameter of the near-field signal is infinite, the algorithm for the near-field source localization is also suitable for estimating the direction of arrival (DOA) of far-field signals. By decomposing the first and second exponent term of the steering vector, the three-dimensional (3-D) parameter is transformed into two-dimensional (2-D) and one-dimensional (1-D) parameter estimation. First, by partitioning the received data, we exploit propagator to acquire the noise subspace. Next, the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA. At last, the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector, and the least squares (LS) is performed to acquire the range parameters. In comparison to the existing algorithms, the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum, which can achieve satisfactory localization and reduce computational complexity. Simulations are implemented to illustrate the advantages of the proposed DIDE method. Moreover, simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.

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Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection
Yongbin YU, Haowen TANG, Xiao FENG, Xiangxiang WANG, Hang HUANG
Journal of Systems Engineering and Electronics    2023, 34 (3): 641-649.   DOI: 10.23919/JSEE.2022.000127
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Memristor with memory properties can be applied to connection points (synapses) between cells in a cellular neural network (CNN). This paper highlights memristor crossbar-based multilayer CNN (MCM-CNN) and its application to edge detection. An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors. MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation. Figure of merit (FOM) is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results. Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

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GNSS array receiver faced with overloaded interferences: anti-jamming performance and the incident directions of interferences
Jie WANG, Wenxiang LIU, Feiqiang CHEN, Zukun LU, Gang OU
Journal of Systems Engineering and Electronics    2023, 34 (2): 335-341.   DOI: 10.23919/JSEE.2022.000072
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Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.

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Sparsity-based efficient simulation of cluster targets electromagnetic scattering
Yuguang TIAN, Yixin LIU, Xuan CHEN, Penghui CHEN, Jun WANG, Junwen CHEN
Journal of Systems Engineering and Electronics    2023, 34 (2): 299-306.   DOI: 10.23919/JSEE.2023.000055
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An efficient and real-time simulation method is proposed for the dynamic electromagnetic characteristics of cluster targets to meet the requirements of engineering practical applications. First, the coordinate transformation method is used to establish a geometric model of the observation scene, which is described by the azimuth angles and elevation angles of the radar in the target reference frame and the attitude angles of the target in the radar reference frame. Then, an approach for dynamic electromagnetic scattering simulation is proposed. Finally, a fast-computing method based on sparsity in the time domain, space domain, and frequency domain is proposed. The method analyzes the sparsity-based dynamic scattering characteristic of the typical cluster targets. The error between the sparsity-based method and the benchmark is small, proving the effectiveness of the proposed method.

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Design and analysis of active disturbance rejection control for time-delay systems using frequency-sweeping
Yongshuai WANG, Zengqiang CHEN, Mingwei SUN, Qinglin SUN
Journal of Systems Engineering and Electronics    2023, 34 (2): 479-491.   DOI: 10.23919/JSEE.2023.000046
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For the typical first-order systems with time-delay, this paper explors the control capability of linear active disturbance rejection control (LADRC). Firstly, the critical time-delay of LADRC is analyzed using the frequency-sweeping method and the Routh criterion, and the stable time-delay interval starting from zero is accurately obtained, which reveals the limitations of general LADRC on large time-delay. Then in view of the large time-delay, an LADRC controller is developed and verified to be effective, along with the robustness analysis. Finally, numerical simulations show the accuracy of critical time-delay, and demonstrate the effectiveness and robustness of the proposed controller compared with other modified LADRCs.

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Role-based Bayesian decision framework for autonomous unmanned systems
Weijian PANG, Xinyi MA, Xueming LIANG, Xiaogang LIU, Erwa DONG
Journal of Systems Engineering and Electronics    2023, 34 (6): 1397-1408.   DOI: 10.23919/JSEE.2023.000114
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In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.

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Triad-displaced ULAs configuration for non-circular sources with larger continuous virtual aperture and enhanced degrees of freedom
Abdul Hayee SHAIKH, Xiaoyu DANG, Daqing HUANG
Journal of Systems Engineering and Electronics    2024, 35 (1): 81-93.   DOI: 10.23919/JSEE.2022.000128
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Non-uniform linear array (NULA) configurations are well renowned due to their structural ability for providing increased degrees of freedom (DOF) and wider array aperture than uniform linear arrays (ULAs). These characteristics play a significant role in improving the direction-of-arrival (DOA) estimation accuracy. However, most of the existing NULA geometries are primarily applicable to circular sources (CSs), while they limitedly improve the DOF and continuous virtual aperture for non-circular sources (NCSs). Toward this purpose, we present a triad-displaced ULAs (Tdis-ULAs) configuration for NCS. The Tdis-ULAs structure generally consists of three ULAs, which are appropriately placed. The proposed antenna array approach fully exploits the non-circular characteristics of the sources. Given the same number of elements, the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors. Advantageously, the number of uniform DOF, optimal distribution of elements among the ULAs, and precise element positions are uniquely determined by the closed-form expressions. Moreover, the proposed array also produces a filled resulting co-array. Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.

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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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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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Wind turbine clutter mitigation using morphological component analysis with group sparsity
Xiaoyu WAN, Mingwei SHEN, Di WU, Daiyin ZHU
Journal of Systems Engineering and Electronics    2023, 34 (3): 714-722.   DOI: 10.23919/JSEE.2022.000157
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To address the problem that dynamic wind turbine clutter (WTC) significantly degrades the performance of weather radar, a WTC mitigation algorithm using morphological component analysis (MCA) with group sparsity is studied in this paper. The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo. After that, the MCA algorithm is applied and the window used in the short-time Fourier transform (STFT) is optimized to lessen the spectrum leakage of WTC. Finally, the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution, thus contributing to better estimation performance of weather signals. The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

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Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics
Jing TIAN, Wei ZHANG
Journal of Systems Engineering and Electronics    2024, 35 (1): 24-30.   DOI: 10.23919/JSEE.2024.000025
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In engineering application, there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval (PRI). Therefore, if the training samples used to calculate the weight vector does not contain the jamming, then the jamming cannot be removed by adaptive spatial filtering. If the weight vector is constantly updated in the range dimension, the training data may contain target echo signals, resulting in signal cancellation effect. To cope with the situation that the training samples are contaminated by target signal, an iterative training sample selection method based on non-homogeneous detector (NHD) is proposed in this paper for updating the weight vector in entire range dimension. The principle is presented, and the validity is proven by simulation results.

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Revised barrier function-based adaptive finite- and fixed-time convergence super-twisting control
Dakai LIU, Sven ESCHE
Journal of Systems Engineering and Electronics    2023, 34 (3): 775-782.   DOI: 10.23919/JSEE.2023.000071
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This paper presents an adaptive gain, finite- and fixed-time convergence super-twisting-like algorithm based on a revised barrier function, which is robust to perturbations with unknown bounds. It is shown that this algorithm can ensure a finite- and fixed-time convergence of the sliding variable to the equilibrium, no matter what the initial conditions of the system states are, and maintain it there in a predefined vicinity of the origin without violation. Also, the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control. Moreover, it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function. This feature will be beneficial when the algorithm is implemented in practice. After that, the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time $ t = 0 $ is discarded. Finally, the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.

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A goal-based approach for modeling and simulation of different types of system-of-systems
Yimin FENG, Chenchu ZHOU, Qiang ZOU, Yusheng LIU, Jiyuan LYU, Xinfeng WU
Journal of Systems Engineering and Electronics    2023, 34 (3): 627-640.   DOI: 10.23919/JSEE.2023.000084
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A system of systems (SoS) composes a set of independent constituent systems (CSs), where the degree of authority to control the independence of CSs varies, depending on different SoS types. Key researchers describe four SoS types with descending levels of central authority: directed, acknowledged, collaborative and virtual. Although the definitions have been recognized in SoS engineering, what is challenging is the difficulty of translating these definitions into models and simulation environments. Thus, we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations. First, we construct the theoretical models of CS and SoS. Based on the theoretical models, we analyze the degree of authority influenced by SoS characteristics. Next, we propose a definition of SoS types by quantitatively explaining the degree of authority. Finally, we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agent-based model (ABM) simulation. This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS, so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.

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Dynamic event-triggered formation control of second-order nonholonomic systems
Xiaoyu WANG, Sijia SUN, Feng XIAO, Mei YU
Journal of Systems Engineering and Electronics    2023, 34 (2): 501-514.   DOI: 10.23919/JSEE.2023.000049
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In this paper, the formation control problem of second-order nonholonomic mobile robot systems is investigated in a dynamic event-triggered scheme. Event-triggered control protocols combined with persistent excitation (PE) conditions are presented. In event-detecting processes, an inactive time is introduced after each sampling instant, which can ensure a positive minimum sampling interval. To increase the flexibility of the event-triggered scheme, internal dynamic variables are included in event-triggering conditions. Moreover, the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive events. In addition, event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation (low-pass) filters. The asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

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Pythagorean probabilistic hesitant triangular fuzzy aggregation operators with applications in multiple attribute decision making
Fuping LIAO, Wu LI, Gang LIU, Xiaoqiang ZHOU
Journal of Systems Engineering and Electronics    2023, 34 (2): 422-438.   DOI: 10.23919/JSEE.2023.000015
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As a generalization of fuzzy set, hesitant probabilistic fuzzy set and pythagorean triangular fuzzy set have their own unique advantages in describing decision information. As modern socioeconomic decision-making problems are becoming more and more complex, it also becomes more and more difficult to appropriately depict decision makers’ cognitive information in decision-making process. In order to describe the decision information more comprehensively, we define a pythagorean probabilistic hesitant triangular fuzzy set (PPHTFS) by combining the pythagorean triangular fuzzy set and the probabilistic hesitant fuzzy set. Firstly, the basic operation and scoring function of the pythagorean probabilistic hesitant triangular fuzzy element (PPHTFE) are proposed, and the comparison rule of two PPHTFEs is given. Then, some pythagorean probabilistic hesitant triangular fuzzy aggregation operators are developed, and their properties are also studied. Finally, a multi-attribute decision-making (MADM) model is constructed based on the proposed operators under the pythagorean probabilistic hesitant triangular fuzzy information, and an illustration example is given to demonstrate the practicability and validity of the proposed decision-making method.

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Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference
Tao JIAN, Jia HE, Bencai WANG, Yu LIU, Congan XU, Zikeng XIE
Journal of Systems Engineering and Electronics    2024, 35 (1): 43-54.   DOI: 10.23919/JSEE.2023.000147
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Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix. The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates. Relying on the two-step criteria, two adaptive detectors based on Gradient tests are proposed, in homogeneous and partially homogeneous clutter plus subspace interference, respectively. Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level. Numerical results show that, the proposed detectors have better performance than their existing counterparts, especially for mismatches in the signal steering vectors.

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Modular flexible “Tetris” microsatellite platform based on sandwich assembly mode
Jun ZHOU, Hao ZHANG, Guanghui LIU, Cheng CHENG, Jiaolong ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 924-938.   DOI: 10.23919/JSEE.2023.000094
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In this paper, a flexible modular “Tetris” microsatellite platform is studied to implement the rapid integration and assembly of microsatellites. The proposed microsatellite platform is fulfilled based on a sandwich assembly mode which consists of the isomorphic module structure and the standard mechanical-electric-data-thermal interfaces. The advantages of the sandwich assembly mode include flexible reconfiguration and efficient assembly. The prototype of the sandwich assembly mode is built for verifying the performance and the feasibility of the proposed mechanical-electric-data-thermal interfaces. Finally, an assembly case is accomplished to demonstrate the validity and advantages of the proposed “Tetris” microsatellite platform.

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PRI modulation recognition and sequence search under small sample prerequisite
Chunjie ZHANG, Yuchen LIU, Weijian SI
Journal of Systems Engineering and Electronics    2023, 34 (3): 706-713.   DOI: 10.23919/JSEE.2023.000007
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Pulse repetition interval (PRI) modulation recognition and pulse sequence search are significant for effective electronic support measures. In modern electromagnetic environments, different types of inter-pulse slide radars are highly confusing. There are few available training samples in practical situations, which leads to a low recognition accuracy and poor search effect of the pulse sequence. In this paper, an approach based on bi-directional long short-term memory (BiLSTM) networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed. The simulation results demonstrate that the proposed algorithm can recognize unilinear, bilinear, sawtooth, and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.

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An AutoML based trajectory optimization method for long-distance spacecraft pursuit-evasion game
Fuyunxiang YANG, Leping YANG, Yanwei ZHU
Journal of Systems Engineering and Electronics    2023, 34 (3): 754-765.   DOI: 10.23919/JSEE.2023.000060
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Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game, which is an interception problem with a non-cooperative maneuvering target. The paper presents an automated machine learning (AutoML) based method to generate optimal trajectories in long-distance scenarios. Compared with conventional deep neural network (DNN) methods, the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise. Firstly, based on differential game theory and costate normalization technique, the trajectory optimization problem is formulated under the assumption of continuous thrust. Secondly, the AutoML technique based on sequential model-based optimization (SMBO) framework is introduced to automate DNN design in deep learning process. If recommended DNN architecture exists, the tree-structured Parzen estimator (TPE) is used, otherwise the efficient neural architecture search (NAS) with network morphism is used. Thus, a novel trajectory optimization method with high computational efficiency is achieved. Finally, numerical results demonstrate the feasibility and efficiency of the proposed method.

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Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold
Fengfei WANG, Shengjin TANG, Liang LI, Xiaoyan SUN, Chuanqiang YU, Xiaosheng SI
Journal of Systems Engineering and Electronics    2023, 34 (2): 530-542.   DOI: 10.23919/JSEE.2023.000042
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Remaining useful life (RUL) prediction is one of the most crucial components in prognostics and health management (PHM) of aero-engines. This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold. Firstly, a random-coefficient regression (RCR) model is used to model the degradation process of aero-engines. Then, the RUL distribution based on fixed failure threshold is derived. The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation (MLE) method and the random coefficient is updated in real time under the Bayesian framework. The failure threshold in this paper is defined by the actual degradation process of aero-engines. After that, a expectation maximization (EM) algorithm is proposed to estimate the underlying failure threshold of aero-engines. In addition, the conditional probability is used to satisfy the limitation of failure threshold. Then, based on above results, an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold (RFT) is derived in a closed-form. Finally, a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

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Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment
Yang ZHAO, Jicheng LIU, Ju JIANG, Ziyang ZHEN
Journal of Systems Engineering and Electronics    2023, 34 (4): 1007-1019.   DOI: 10.23919/JSEE.2023.000102
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The dynamic weapon target assignment (DWTA) problem is of great significance in modern air combat. However, DWTA is a highly complex constrained multi-objective combinatorial optimization problem. An improved elitist non-dominated sorting genetic algorithm-II (NSGA-II) called the non-dominated shuffled frog leaping algorithm (NSFLA) is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints. In NSFLA, the shuffled frog leaping algorithm (SFLA) is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm (GA), displaying low optimization speed and heterogeneous space search defects. Two improvements have also been raised to promote the internal optimization performance of SFLA. Firstly, the local evolution scheme, a novel crossover mechanism, ensures that each individual participates in updating instead of only the worst ones, which can expand the diversity of the population. Secondly, a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search. Finally, the scheme is verified in various air combat scenarios. The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency, especially in many aircraft and the dynamic air combat environment.

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Persymmetric adaptive polarimetric detection of subspace range-spread targets in compound Gaussian sea clutter
Shuwen XU, Yifan HAO, Zhuo WANG, Jian XUE
Journal of Systems Engineering and Electronics    2024, 35 (1): 31-42.   DOI: 10.23919/JSEE.2023.000133
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This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and non-Gaussian sea clutter. The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters, and the target is modeled as a subspace range-spread target model. The persymmetric structure is used to model the clutter covariance matrix, in order to reduce the reliance on secondary data of the designed detectors. Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test (GLRT), Rao test, and Wald test. All the proposed detectors have constant false-alarm rate property with respect to the clutter texture, the speckle covariance matrix. Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments, and the proposed GLRT detector has the best detection performance under different parameters.

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A UAV collaborative defense scheme driven by DDPG algorithm
Yaozhong ZHANG, Zhuoran WU, Zhenkai XIONG, Long CHEN
Journal of Systems Engineering and Electronics    2023, 34 (5): 1211-1224.   DOI: 10.23919/JSEE.2023.000128
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The deep deterministic policy gradient (DDPG) algorithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration. Using the DDPG algorithm, agents can explore and summarize the environment to achieve autonomous decisions in the continuous state space and action space. In this paper, a cooperative defense with DDPG via swarms of unmanned aerial vehicle (UAV) is developed and validated, which has shown promising practical value in the effect of defending. We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process. The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently, meeting the requirements of a UAV swarm for non-centralization, autonomy, and promoting the intelligent development of UAVs swarm as well as the decision-making process.

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Minimum eigenvalue based adaptive fault compensation for hypersonic vehicles
Yajie MA, Bin JIANG, Hao REN
Journal of Systems Engineering and Electronics    2023, 34 (2): 492-500.   DOI: 10.23919/JSEE.2023.000039
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The attitude tracking control problem is addressed for hypersonic vehicles under actuator faults that may cause an uncertain time-varying control gain matrix. An adaptive compensation scheme is developed to ensure system stability and asymptotic tracking properties, including a kinematic control signal and a dynamic control signal. To deal with the uncertainties of the control gain matrix, a new positive definite one is constructed. The minimum eigenvalue of such a new control gain matrix is estimated. Simulation results of application to an X-33 vehicle model verify the effectiveness of the proposed minimum eigenvalue based adaptive fault compensation scheme.

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Finite-time fault-tolerant control of teleoperating cyber physical system against faults
Chengwei PAN, Xia LIU, Yong CHEN, Meng LI
Journal of Systems Engineering and Electronics    2023, 34 (2): 469-478.   DOI: 10.23919/JSEE.2023.000044
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This paper studies a finite-time adaptive fractional-order fault-tolerant control (FTC) scheme for the slave position tracking of the teleoperating cyber physical system (TCPS) with external disturbances and actuator faults. Based on the fractional Lyapunov stability theory and the finite-time stability theory, a fractional-order nonsingular fast terminal sliding mode (FO-NFTSM) control law is proposed to promote the tracking and fault tolerance performance of the considered system. Meanwhile, the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances. Furthermore, the finite-time stability of the closed-loop system is proved. Finally, comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.

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