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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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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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Disparity estimation for multi-scale multi-sensor fusion
Guoliang SUN, Shanshan PEI, Qian LONG, Sifa ZHENG, Rui YANG
Journal of Systems Engineering and Electronics    2024, 35 (2): 259-274.   DOI: 10.23919/JSEE.2023.000101
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The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

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A framework of force of information influence and application for C4KISR system
Shaojie MAO, Lianwang DIAO, Yu SUN, Heng WANG, Kan YI, Xin XU, Xiaobin MAO, Kecheng ZHANG, Long SHENG
Journal of Systems Engineering and Electronics    2024, 35 (2): 430-443.   DOI: 10.23919/JSEE.2024.000011
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The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence, surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing, decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

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Manipulator tracking technology based on FSRUKF
Guoqing SHI, Boyan ZHANG, Jiandong ZHANG, Qiming YANG, Xiaofeng HUANG, Jianyao QUE, Junwei PU, Xiutang GENG
Journal of Systems Engineering and Electronics    2024, 35 (2): 473-484.   DOI: 10.23919/JSEE.2024.000009
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Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment, the key technologies such as machine vision and manipulator control are studied, and a complete manipulator vision tracking system is designed. Firstly, Denavit-Hartenberg (D-H) parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator. At the same time, a binocular camera is used to obtain the three-dimensional position of the target. Secondly, in order to make the manipulator track the target more accurately, the fuzzy adaptive square root unscented Kalman filter (FSRUKF) is proposed to estimate the target state. Finally, the manipulator tracking system is built by using the position-based visual servo. The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter (SRUKF), which meets the application requirements of the manipulator tracking system, and basically meets the application requirements of the manipulator tracking system in the practical experiments.

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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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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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Short-time maritime target detection based on polarization scattering characteristics
Shichao CHEN, Feng LUO, Min TIAN, Wanghan LYU
Journal of Systems Engineering and Electronics    2024, 35 (1): 55-64.   DOI: 10.23919/JSEE.2023.000148
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In this paper, a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed. Firstly, the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level. Due to the artificial material structure on the surface of the target, it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell. Then, based on the analysis of the decomposition results, a new feature with scattering geometry characteristics in polarization domain, denoted as Cameron polarization decomposition scattering weight (CPD-SW), is extracted as the test statistic, which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types. Finally, the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset, which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

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Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics
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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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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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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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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Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system
Yuqi YUAN, Di ZHOU, Junlong LI, Chaofei LOU
Journal of Systems Engineering and Electronics    2024, 35 (2): 451-462.   DOI: 10.23919/JSEE.2024.000008
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In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory (LSTM) neural network is nested into the extended Kalman filter (EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states, an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.

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Fast solution to the free return orbit’s reachable domain of the manned lunar mission by deep neural network
Luyi YANG, Haiyang LI, Jin ZHANG, Yuehe ZHU
Journal of Systems Engineering and Electronics    2024, 35 (2): 495-508.   DOI: 10.23919/JSEE.2023.000117
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It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than ${0.01^ \circ }$ on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

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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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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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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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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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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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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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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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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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Journal of Systems Engineering and Electronics    2023, 34 (5): 0-.  
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Event-triggered model-free adaptive control for a class of surface vessels with time-delay and external disturbance via state observer
Hua CHEN, Chao SHEN, Jiahui HUANG, Yuhan CAO
Journal of Systems Engineering and Electronics    2023, 34 (3): 783-797.   DOI: 10.23919/JSEE.2023.000075
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This paper provides an improved model-free adaptive control (IMFAC) strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance. Firstly, the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization (local-CFDL). To take advantage of the resulting structure, use a discrete-time extended state observer (DESO) to estimate the unknown residual factor. Then, according to the study, the inclusion of a time delay has no effect on the linearization structure, and an improved control approach is provided, in which DESO is used to adjust for uncertainties. Furthermore, a DESO-based event-triggered model-free adaptive control (ET-DESO-MFAC) is established by designing event-triggered conditions to assure Lyapunov stability. Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated; otherwise, the control input will stay the same as it is at the last trigger moment. A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking. Finally, simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.

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A modified OMP method for multi-orbit three dimensional ISAR imaging of the space target
Libing JIANG, Shuyu ZHENG, Qingwei YANG, Peng YANG, Zhuang WANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 879-893.   DOI: 10.23919/JSEE.2023.000066
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The conventional two dimensional (2D) inverse synthetic aperture radar (ISAR) imaging fails to provide the targets’ three dimensional (3D) information. In this paper, a 3D ISAR imaging method for the space target is proposed based on mutli-orbit observation data and an improved orthogonal matching pursuit (OMP) algorithm. Firstly, the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension. Then, an improved OMP algorithm is applied to recover the space target’s amplitude information via the 2D matrix data. Finally, scattering centers can be reconstructed with specific three dimensional locations. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

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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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Attention mechanism based multi-scale feature extraction of bearing fault diagnosis
Xue LEI, Ningyun LU, Chuang CHEN, Tianzhen HU, Bin JIANG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1359-1367.   DOI: 10.23919/JSEE.2023.000129
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Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery. In practical applications, bearings often work at various rotational speeds as well as load conditions. Yet, the bearing fault diagnosis under multiple conditions is a new subject, which needs to be further explored. Therefore, a multi-scale deep belief network (DBN) method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals, containing four primary steps: preprocessing of multi-scale data, feature extraction, feature fusion, and fault classification. The key novelties include multi-scale feature extraction using multi-scale DBN algorithm, and feature fusion using attention mechanism. The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method. Furthermore, the aforementioned method is compared with four classical fault diagnosis methods reported in the literature, and the comparison results show that our proposed method has higher diagnostic accuracy and better robustness.

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Journal of Systems Engineering and Electronics    2023, 34 (4): 0-.  
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Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults
Zhengyu YE, Bin JIANG, Yuehua CHENG, Ziquan YU, Yang YANG
Journal of Systems Engineering and Electronics    2023, 34 (3): 766-774.   DOI: 10.23919/JSEE.2023.000047
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Component failures can cause multi-agent system (MAS) performance degradation and even disasters, which provokes the demand of the fault diagnosis method. A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults. Firstly, the actuator and sensor faults are extended to the system state, and the system is transformed into a descriptor system form. Then, a sliding mode-based distributed unknown input observer is proposed to estimate the extended state. Furthermore, adaptive laws are introduced to adjust the observer parameters. Finally, the effectiveness of the proposed method is demonstrated with numerical simulations.

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Super-resolution parameter estimation of monopulse radar by wide-narrowband joint processing
Tianyi CAI, Bo DAN, Weibo HUANG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1158-1170.   DOI: 10.23919/JSEE.2023.000132
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The angular resolution of radar is of crucial significance to its tracking performance. In this paper, a super-resolution parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar. The range cells containing resolvable scattering points are detected in the wideband mode, and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement. Then, the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters, and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy. Simulation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mismatch.

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Reliability-based selective maintenance for redundant systems with dependent performance characteristics of components
Hui CAO, Fuhai DUAN, Yu’nan DUAN
Journal of Systems Engineering and Electronics    2023, 34 (3): 804-814.   DOI: 10.23919/JSEE.2023.000086
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The reliability-based selective maintenance (RSM) decision problem of systems with components that have multiple dependent performance characteristics (PCs) reflecting degradation states is addressed in this paper. A vine-Copula-based reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs. Specifically, the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism. The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine. In addition, two RSM decision models are developed to ensure the system accomplishes the next mission. The genetic algorithm (GA) is used to solve the constraint optimization problem of the models. A numerical example illustrates the application of the proposed RSM method.

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Dual-stream coupling network with wavelet transform for cross-resolution person re-identification
Rui SUN, Zi YANG, Zhenghui ZHAO, Xudong ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (3): 682-695.   DOI: 10.23919/JSEE.2023.000028
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Person re-identification is a prevalent technology deployed on intelligent surveillance. There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution, yet such models are not applicable to the open world. In real world, the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent. When low-resolution (LR) images in the query set are matched with high-resolution (HR) images in the gallery set, it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images. To address the above issues, we present a dual-stream coupling network with wavelet transform (DSCWT) for the cross-resolution person re-identification task. Firstly, we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images, which is applied to restore the lost detail information of LR images. Then, we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions. Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

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Journal of Systems Engineering and Electronics    2024, 35 (1): 0-.  
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Reliability analysis for wireless communication networks via dynamic Bayesian network
Shunqi YANG, Ying ZENG, Xiang LI, Yanfeng LI, Hongzhong HUANG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1368-1374.   DOI: 10.23919/JSEE.2023.000130
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The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices, radio propagation, network topology, and dynamic behaviors. Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks. As one of the most popular modeling methodologies, the dynamic Bayesian network (DBN) is proposed. However, it is insufficient for the wireless communication network which contains temporal and non-temporal events. To this end, we present a modeling methodology for a generalized continuous time Bayesian network (CTBN) with a 2-state conditional probability table (CPT). Moreover, a comprehensive reliability analysis method for communication devices and radio propagation is suggested. The proposed methodology is verified by a reliability analysis of a real wireless communication network.

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Leader trajectory planning method considering constraints of formation controller
Dongdong YAO, Xiaofang WANG, Hai LIN, Zhuping WANG
Journal of Systems Engineering and Electronics    2023, 34 (5): 1294-1308.   DOI: 10.23919/JSEE.2023.000079
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To ensure safe flight of multiple fixed-wing unmanned aerial vehicles (UAVs) formation, considering trajectory planning and formation control together, a leader trajectory planning method based on the sparse A* algorithm is introduced. Firstly, a formation controller based on prescribed performance theory is designed to control the transient and steady formation configuration, as well as the formation forming time, which not only can form the designated formation configuration but also can guarantee collision avoidance and terrain avoidance theoretically. Next, considering the constraints caused by formation controller on trajectory planning such as the safe distance, turn angle and step length, as well as the constraint of formation shape, a leader trajectory planning method based on sparse A* algorithm is proposed. Simulation results show that the UAV formation can arrive at the destination safely with a short trajectory no matter keeping the formation or encountering formation transformation.

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Aerial target threat assessment based on gated recurrent unit and self-attention mechanism
Chen CHEN, Wei QUAN, Zhuang SHAO
Journal of Systems Engineering and Electronics    2024, 35 (2): 361-373.   DOI: 10.23919/JSEE.2023.000116
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Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit (SA-GRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform (FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features. Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

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Effective implementation and improvement of fast labeled multi-Bernoulli filter
Xuan CHENG, Hongbing JI, Yongquan ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (3): 661-673.   DOI: 10.23919/JSEE.2023.000030
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Effective implementation of the fast labeled multi-Bernoulli (FLMB) filter is addressed for target tracking with interval measurements. Firstly, a sequential Monte Carlo (SMC) implementation of the FLMB filter, SMC-FLMB filter, is derived based on generalized likelihood function weighting. Then, a box particle (BP) implementation of the FLMB filter, BP-FLMB filter, is developed, with a computational complexity reduction of the SMC-FLMB filter. Finally, an improved version of the BP-FLMB filter, improved BP-FLMB (IBP-FLMB) filter, is proposed, improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter. Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter, with similar tracking performance. Compared with the BP-FLMB filter, the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

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A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts
Jian’gang PENG, Guang XIA, Baoqun SUN, Shaojie WANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 966-980.   DOI: 10.23919/JSEE.2022.000130
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This paper presents an operational framework of unstructured decision-making approach involving quality function deployment (QFD) in an uncertain linguistic context. Firstly, QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment. Secondly, hesitant fuzzy linguistic term sets (HFLTSs), which facilitate the management and handling of information equivocality, are designed to construct a house of quality (HoQ) in the product planning process. The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets. Thirdly, a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization. The inter-relationships of cooperative partners are directly matched with a back propagation neural network (BPNN) to construct the multi-enterprise manufacturing network. The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives. Finally, a real-world example, namely, the prototype manufacturing of an automatic transmission for a vehicle, is provided to illustrate the effectiveness of the proposed decision-making approach.

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