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Research on infrared dim and small target detection algorithm based on low-rank tensor recovery
Chuntong LIU, Hao WANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 861-872.   DOI: 10.23919/JSEE.2023.000004
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In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics, and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness. Finally, the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image, and the final small target is worked out by a simple partitioning algorithm. The test results in various space-based downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate. It is a kind of infrared small and dim target detection method with good performance.

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets
Haipeng JIANG, Guoqing WU, Mengdan SUN, Feng LI, Yunfei SUN, Wei FANG
Journal of Systems Engineering and Electronics    2024, 35 (4): 965-975.   DOI: 10.23919/JSEE.2024.000020
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Evolutionary algorithms (EAs) have been used in high utility itemset mining (HUIM) to address the problem of discovering high utility itemsets (HUIs) in the exponential search space. EAs have good running and mining performance, but they still require huge computational resource and may miss many HUIs. Due to the good combination of EA and graphics processing unit (GPU), we propose a parallel genetic algorithm (GA) based on the platform of GPU for mining HUIM (PHUI-GA). The evolution steps with improvements are performed in central processing unit (CPU) and the CPU intensive steps are sent to GPU to evaluate with multi-threaded processors. Experiments show that the mining performance of PHUI-GA outperforms the existing EAs. When mining 90% HUIs, the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

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Consensus model of social network group decision-making based on trust relationship among experts and expert reliability
Ya WANG, Mei CAI, Xinglian JIAN
Journal of Systems Engineering and Electronics    2023, 34 (6): 1576-1588.   DOI: 10.23919/JSEE.2023.000021
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Due to people’s increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts’ preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts ’ importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

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A tunable adaptive detector for distributed targets when signal mismatch occurs
Yufeng CUI, Yongliang WANG, Weijian LIU, Qinglei DU, Xichuan ZHANG, Xuhui LI
Journal of Systems Engineering and Electronics    2023, 34 (4): 873-878.   DOI: 10.23919/JSEE.2023.000029
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Aiming at the problem of detecting a distributed target when signal mismatch occurs, this paper proposes a tunable detector parameterized by an adjustable parameter. By adjusting the parameter, the tunable detector can achieve robust or selective detection of mismatched signals. Moreover, the proposed tunable detector, with a proper tunable parameter, can provide higher detection probability compared with existing detectors in the case of no signal mismatch. In addition, the proposed tunable detector possesses the constant false alarm rate property with the unknown noise covariance matrix.

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Efficient unequal error protection for online fountain codes
Pengcheng SHI, Zhenyong WANG, Dezhi LI, Haibo LYU
Journal of Systems Engineering and Electronics    2024, 35 (2): 286-293.   DOI: 10.23919/JSEE.2022.000156
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In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.

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Fault diagnosis method of link control system for gravitational wave detection
Ai GAO, Shengnan XU, Zichen ZHAO, Haibin SHANG, Rui XU
Journal of Systems Engineering and Electronics    2024, 35 (4): 922-931.   DOI: 10.23919/JSEE.2024.000048
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To maintain the stability of the inter-satellite link for gravitational wave detection, an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed. Different from the traditional fault diagnosis optimization algorithms, the fault intelligent learning method proposed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong coupling nonlinearity. By constructing a two-layer learning network, the method enables efficient joint diagnosis of fault areas and fault parameters. The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s, and the fault diagnosis efficiency is improved by 99.8% compared with the traditional algorithm.

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Cloud-based predictive adaptive cruise control considering preceding vehicle and slope information
Bolin GAO, Luyao WANG, Shuyan LI, Keke WAN, Xuepeng WANG, Jin ZHANG, Chen WANG, Yanbin LIU, Wei ZHONG
Journal of Systems Engineering and Electronics    2024, 35 (6): 1542-1562.   DOI: 10.23919/JSEE.2024.000108
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With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess, the cloud control system (CCS) has exhibited formidable potential in the realm of connected assisted driving, such as the adaptive cruise control (ACC). Based on the CCS architecture, this paper proposes a cloud-based predictive ACC (PACC) strategy, which fully considers the road slope information and the preceding vehicle status. In the cloud, based on the dynamic programming (DP), the long-term economic speed planning is carried out by using the slope information. At the vehicle side, the real-time fusion planning of the economic speed and the preceding vehicle state is realized based on the model predictive control (MPC), taking into account the safety and economy of driving. In order to ensure the safety and stability of the vehicle-cloud cooperative control system, an event-triggered cruise mode switching method is proposed based on the state of each subsystem of the vehicle-cloud-network-map. Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions. Moreover, under normal conditions, compared to the ACC system, the PACC system can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle, thus achieving fuel savings of 3% to 8%.

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A process monitoring method for autoregressive-dynamic inner total latent structure projection
Yalin CHEN, Xiangyu KONG, Jiayu LUO
Journal of Systems Engineering and Electronics    2024, 35 (5): 1326-1336.   DOI: 10.23919/JSEE.2024.000105
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As a dynamic projection to latent structures (PLS) method with a good output prediction ability, dynamic inner PLS (DiPLS) is widely used in the prediction of key performance indicators. However, due to the oblique decomposition of the input space by DiPLS, there are false alarms in the actual industrial process during fault detection. To address the above problems, a dynamic modeling method based on autoregressive-dynamic inner total PLS (AR-DiTPLS) is proposed. The method first uses the regression relation matrix to decompose the input space orthogonally, which reduces useless information for the prediction output in the quality-related dynamic subspace. Then, a vector autoregressive model (VAR) is constructed for the prediction score to separate dynamic information and static information. Based on the VAR model, appropriate statistical indicators are further constructed for online monitoring, which reduces the occurrence of false alarms. The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

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UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience
Guang ZHAN, Kun ZHANG, Ke LI, Haiyin PIAO
Journal of Systems Engineering and Electronics    2024, 35 (3): 644-665.   DOI: 10.23919/JSEE.2024.000022
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Autonomous umanned aerial vehicle (UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decision-making policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods. Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes (MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

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A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system
Xu LYU, Ziyang MENG, Chunyu LI, Zhenyu CAI, Yi HUANG, Xiaoyong LI, Xingkai YU
Journal of Systems Engineering and Electronics    2024, 35 (3): 732-740.   DOI: 10.23919/JSEE.2024.000060
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In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

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Belief exponential divergence for D-S evidence theory and its application in multi-source information fusion
Xiaobo DUAN, Qiucen FAN, Wenhao BI, An ZHANG
Journal of Systems Engineering and Electronics    2024, 35 (6): 1454-1468.   DOI: 10.23919/JSEE.2024.000101
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Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion. Nevertheless, when fusing highly conflicting evidence it may produce counterintuitive outcomes. To address this issue, a fusion approach based on a newly defined belief exponential divergence and Deng entropy is proposed. First, a belief exponential divergence is proposed as the conflict measurement between evidences. Then, the credibility of each evidence is calculated. Afterwards, the Deng entropy is used to calculate information volume to determine the uncertainty of evidence. Then, the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence. Ultimately, initial evidences are amended and fused using Dempster’s rule of combination. The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic examples. Additionally, the proposed approach is applied to aerial target recognition and iris dataset-based classification to validate its efficacy. Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.

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Intelligent recognition and information extraction of radar complex jamming based on time-frequency features
Ruihui PENG, Xingrui WU, Guohong WANG, Dianxing SUN, Zhong YANG, Hongwen LI
Journal of Systems Engineering and Electronics    2024, 35 (5): 1148-1166.   DOI: 10.23919/JSEE.2024.000073
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In modern war, radar countermeasure is becoming increasingly fierce, and the enemy jamming time and pattern are changing more randomly. It is challenging for the radar to efficiently identify jamming and obtain precise parameter information, particularly in low signal-to-noise ratio (SNR) situations. In this paper, an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue. Firstly, a joint algorithm based on YOLOv5 convolutional neural networks (CNNs) is proposed, which is used to achieve the jamming signal classification and preliminary parameter estimation. Furthermore, an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test, feature region search, position regression, spectrum interpolation, etc., which realizes the accurate estimation of jamming carrier frequency, relative delay, Doppler frequency shift, and other parameters. Finally, the approach has improved performance for complex jamming recognition and parameter estimation under low SNR, and the recognition rate can reach 98% under ?15 dB SNR, according to simulation and real data verification results.

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Direction finding of bistatic MIMO radar in strong impulse noise
Menghan CHEN, Hongyuan GAO, Yanan DU, Jianhua CHENG, Yuze ZHANG
Journal of Systems Engineering and Electronics    2024, 35 (4): 888-898.   DOI: 10.23919/JSEE.2024.000002
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For bistatic multiple-input multiple-output (MIMO) radar, this paper presents a robust and direction finding method in strong impulse noise environment. By means of a new lower order covariance, the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood (ML) estimation method. A quantum equilibrium optimizer algorithm (QEOA) is devised to resolve the corresponding objective function for efficient and accurate direction finding. The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations, e.g., locating coherent signal sources with very few snapshots in strong impulse noise. Other than that, the Cramér-Rao bound (CRB) under impulse noise environment has been drawn to test the capability of the presented method.

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DCEL: classifier fusion model for Android malware detection
Xiaolong XU, Shuai JIANG, Jinbo ZHAO, Xinheng WANG
Journal of Systems Engineering and Electronics    2024, 35 (1): 163-177.   DOI: 10.23919/JSEE.2024.000018
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The rapid growth of mobile applications, the popularity of the Android system and its openness have attracted many hackers and even criminals, who are creating lots of Android malware. However, the current methods of Android malware detection need a lot of time in the feature engineering phase. Furthermore, these models have the defects of low detection rate, high complexity, and poor practicability, etc. We analyze the Android malware samples, and the distribution of malware and benign software in application programming interface (API) calls, permissions, and other attributes. We classify the software’s threat levels based on the correlation of features. Then, we propose deep neural networks and convolutional neural networks with ensemble learning (DCEL), a new classifier fusion model for Android malware detection. First, DCEL preprocesses the malware data to remove redundant data, and converts the one-dimensional data into a two-dimensional gray image. Then, the ensemble learning approach is used to combine the deep neural network with the convolutional neural network, and the final classification results are obtained by voting on the prediction of each single classifier. Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models, the proposed DCEL has a higher detection rate, higher recall rate, and lower computational cost.

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Robust output regulation problem with prescribed performance for nonlinear strict feedback systems
Haichao ZHU, Weiyao LAN
Journal of Systems Engineering and Electronics    2023, 34 (4): 1033-1041.   DOI: 10.23919/JSEE.2023.000098
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This paper investigates the problem of robust output regulation control with prospected transient property for strict feedback systems. By employing the internal model principle, the robust output regulation problem with a prospected property can be transformed to a robust stabilization problem with a new output constraint. Then, by constructing the speed function and adopting barrier Lyapunov function technique, the dynamic feedback controller can be designed not only to drive error output of the closed-loop system entering into a prescribed performance bound within a given finite time, but also to achieve that the error output converges to zero asymptotically. The effectiveness of the results is illustrated by a simulation example.

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Modified filter for mean elements estimation with state jumping
Yanjun YU, Chengfei YUE, Huayi LI, Yunhua WU, Xueqin CHEN
Journal of Systems Engineering and Electronics    2024, 35 (4): 999-1012.   DOI: 10.23919/JSEE.2024.000081
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To investigate the real-time mean orbital elements (MOEs) estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit transfer, a modified augmented square-root unscented Kalman filter (MASUKF) is proposed. The MASUKF is composed of sigma points calculation, time update, modified state jumping detection, and measurement update. Compared with the filters used in the existing literature on MOEs estimation, it has three main characteristics. Firstly, the state vector is augmented from six to nine by the added thrust acceleration terms, which makes the filter additionally give the state-jumping-thrust-acceleration estimation. Secondly, the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency. Thirdly, when sate jumping is detected, the covariance matrix inflation will be done, and then an extra time update process will be conducted at this time instance before measurement update. In this way, the relatively large estimation error at the detection moment can significantly decrease. Finally, typical simulations are performed to illustrated the effectiveness of the method.

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A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation
Jun TANG, Wanting QIN, Qingtao PAN, Songyang LAO
Journal of Systems Engineering and Electronics    2024, 35 (3): 666-678.   DOI: 10.23919/JSEE.2024.000042
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Natural events have had a significant impact on overall flight activity, and the aviation industry plays a vital role in helping society cope with the impact of these events. As one of the most impactful weather typhoon seasons appears and continues, airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms. This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation. The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules, and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction. With more dependable data accuracy, problems can be analysed rapidly and more efficiently, enabling better decision-making with a proactive versus reactive posture. When multiple modalities coexist, features can be extracted from them simultaneously to supplement each other’s information. An actual case study, the typhoon Lichma that swept China in 2019, has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

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Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process: from two different forms of evaluation information and matching perspective
Xin HUANG, Xiaoyan QI, Hongzhuan CHEN, Xiang CAI
Journal of Systems Engineering and Electronics    2023, 34 (4): 939-954.   DOI: 10.23919/JSEE.2023.000099
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With the development of central-private enterprises integration, selecting suitable key suppliers are able to provide core components for smart complex equipment. We consider selecting suitable key suppliers from matching perspective, for it not only satisfies natural development of smart complex equipment, it is also a good implementation of equipment project in central-private enterprises integration context. In in this paper, we carry out two parts of research, one is evaluation attributes based on comprehensive analysis, and the other is matching process between key suppliers and core components based on the matching attribute. In practical analysis process, we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows. In the analysis process, we adopt entropy-maximum deviation method (MDM)-decision-making trial and evaluation laboratory (DEMATEL)-technique for order preference by similarity to an ideal solution (TOPSIS) to obtain a comprehensive analysis. The entropy-MDM is applied to get weight value, DEMATEL is utilized to obtain internal relations, and TOPSIS is adopted to get ideal evaluated solution. We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean. Moreover, based on the aforementioned attributes and generalized power Heronian mean operator, we aggregate preference information to acquire relevant satisfaction degree, then combine the constructed matching model to get suitable key supplier. Through comprehensive analysis of selecting suitable suppliers, we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment. Through analysis for relevant factors, we find that leading role and service level are also significant for the smart complex equipment development process.

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

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Beidou receiver based on anti-jamming antenna arrays with self-calibration for precise relative positioning
Yi AN, Ronglei KANG, Yalong BAN, Shaoshuai YANG
Journal of Systems Engineering and Electronics    2024, 35 (5): 1132-1147.   DOI: 10.23919/JSEE.2024.000013
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Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.

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Low-power system model for quantum entangled photon-pair source
Tianxuan FENG, Hanyi ZHANG, Rong FAN, Honghao MA, Mengcheng DONG, Lijing LI
Journal of Systems Engineering and Electronics    2024, 35 (5): 1287-1294.   DOI: 10.23919/JSEE.2024.000104
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The quantum entangled photon-pair source, as an essential component of optical quantum systems, holds great potential for applications such as quantum teleportation, quantum computing, and quantum imaging. The current workhorse technique for preparing photon pairs involves performing spontaneous parametric down conversion (SPDC) in bulk nonlinear crystals. However, the current power consumption and cost of preparing entangled photon-pair sources are relatively high, posing challenges to their integration and scalability. In this paper, we propose a low-power system model for the quantum entangled photon-pair source based on SPDC theory and phase matching technology. This model allows us to analyze the performance of each module and the influence of component characteristics on the overall system. In our experimental setup, we utilize a 5 mW laser diode and a typical type-II barium metaborate (BBO) crystal to prepare an entangled photon-pair source. The experimental results are in excellent agreement with the model, indicating a significant step towards achieving the goal of low-power and low-cost entangled photon-pair sources. This achievement not only contributes to the practical application of quantum entanglement lighting, but also paves the way for the widespread adoption of optical quantum systems in the future.

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Equipment damage measurement method of wartime based on FCE-PCA-RF
Mingyu LI, Lu GAO, Hongwei XU, Kai LI, Yisong HUANG
Journal of Systems Engineering and Electronics    2024, 35 (3): 707-719.   DOI: 10.23919/JSEE.2024.000065
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As the “engine” of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation (FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85% of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.

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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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Heterogeneous information fusion recognition method based on belief rule structure
Haibin WANG, Xin GUAN, Xiao YI, Guidong SUN
Journal of Systems Engineering and Electronics    2024, 35 (4): 955-964.   DOI: 10.23919/JSEE.2023.000169
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To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion, but the expert knowledge is not fully utilized, a heterogeneous information fusion recognition method based on belief rule structure is proposed. By defining the continuous probabilistic hesitation fuzzy linguistic term sets (CPHFLTS) and establishing CPHFLTS distance measure, the belief rule base of the relationship between feature space and category space is constructed through information integration, and the evidence reasoning of the input samples is carried out. The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition. Compared with the other methods, the proposed method has a higher correct recognition rate under different noise levels.

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Design of integral sliding mode guidance law based on disturbance observer
Jianping ZHOU, Wenjie ZHANG, Hang ZHOU, Qiang LI, Qunli XIA
Journal of Systems Engineering and Electronics    2024, 35 (1): 186-194.   DOI: 10.23919/JSEE.2023.000111
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With the increasing precision of guidance, the impact of autopilot dynamic characteristics and target maneuvering abilities on precision guidance is becoming more and more significant. In order to reduce or even eliminate the autopilot dynamic operation and the target maneuvering influence, this paper suggests a guidance system model involving a novel integral sliding mode guidance law (ISMGL). The method utilizes the dynamic characteristics and the impact angle, combined with a sliding mode surface scheme that includes the desired line-of-sight angle, line-of-sight angular rate, and second-order differential of the angular line-of-sight. At the same time, the evaluation scenario considere the target maneuvering in the system as the external disturbance, and the non-homogeneous disturbance observer estimate the target maneuvering as a compensation of the guidance command. The proposed system’s stability is proven based on the Lyapunov stability criterion. The simulations reveale that ISMGL effectively intercepted large maneuvering targets and present a smaller miss-distance compared with traditional linear sliding mode guidance laws and trajectory shaping guidance laws. Furthermore, ISMGL has a more accurate impact angle and fast convergence speed.

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Hound: a parallel image distribution system for cluster based on Docker
Zijie LIU, Junjiang LI, Can CHEN, Dengyin ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 955-965.   DOI: 10.23919/JSEE.2023.000105
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Current applications, consisting of multiple replicas, are packaged into lightweight containers with their execution dependencies. Considering the dominant impact of distribution efficiency of gigantic images on container startup (e.g., distributed deep learning application), the image “warm-up” technique which prefetches images of these replicas to destination nodes in the cluster is proposed. However, the current image “warm-up” technique solely focuses on identical image distribution, which fails to take effect when distributing different images to destination nodes. To address this problem, this paper proposes Hound, a simple but efficient cluster image distribution system based on Docker. To support diverse image distribution requests of cluster nodes, Hound additionally adopts node-level parallelism (i.e., downloading images to destination nodes in parallel) to further improve the efficiency of image distribution. The experimental results demonstrate Hound outperforms Docker, kubernetes container runtime interface (CRI-O), and Docker-compose in terms of image distribution performance when cluster nodes request different images. Moreover, the high scalability of Hound is evaluated in the scenario of ten nodes.

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How to implement a knowledge graph completeness assessment with the guidance of user requirements
Ying ZHANG, Gang XIAO
Journal of Systems Engineering and Electronics    2024, 35 (3): 679-688.   DOI: 10.23919/JSEE.2024.000046
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In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume. When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph. However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

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CONTENTS
Journal of Systems Engineering and Electronics    2023, 34 (4): 0-.  
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An accurate detection algorithm for time backtracked projectile-induced water columns based on the improved YOLO network
Yasong LUO, Jianghu XU, Chengxu FENG, Kun ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 981-991.   DOI: 10.23919/JSEE.2023.000106
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During a sea firing training, the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance, while the correct and precise calculation of miss distance is directly affected by the accuracy, false alarm rate and time delay of detection. After analyzing the characteristics of projectile-induced water columns, an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once (YOLO) network is put forward. The capability and accuracy of detecting projectile-induced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation (SE) attention module. The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix (GLCM), so as to effectively eliminate such disturbances as ocean waves and ship wakes, and lower the false alarm rate of projectile-induced water column detection. The improved algorithm increases the mAP50 of water column detection by 30.3%. On the basis of correct detection, a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence. It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images, and considerably reduces detection delay, so as to provide the support for the automatic, accurate, and real-time calculation of miss distance.

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Optimal maneuver strategy to improve the observability of angles-only rendezvous
Ronghua DU, Wenhe LIAO, Xiang ZHANG
Journal of Systems Engineering and Electronics    2023, 34 (4): 1020-1032.   DOI: 10.23919/JSEE.2023.000091
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This paper proposes an optimal maneuver strategy to improve the observability of angles-only rendezvous from the perspective of relative navigation. A set of dimensionless relative orbital elements (ROEs) is used to parameterize the relative motion, and the objective function of the observability of angles-only navigation is established. An analytical solution of the optimal maneuver strategy to improve the observability of angles-only navigation is obtained by means of numerical analysis. A set of dedicated semi-physical simulation system is built to test the performances of the proposed optimal maneuver strategy. Finally, the effectiveness of the method proposed in this paper is verified through the comparative analysis of the objective function of the observability of angles-only navigation and the performances of the angles-only navigation filter under different maneuver schemes. Compared with the cases without orbital maneuver, it is concluded that the tangential filtering accuracy with the optimal orbital maneuver at the terminal time is increased by 35% on average, and the radial and normal filtering accuracy is increased by 30% on average.

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Contact detumbling toward a nutating target through deformable effectors and prescribed performance controller
Yue ZANG, Yao ZHANG, Quan HU, Mou LI, Yujun CHEN
Journal of Systems Engineering and Electronics    2024, 35 (3): 753-768.   DOI: 10.23919/JSEE.2023.000121
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Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling. Finally, by employing the proposed effector and the controller, numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.

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Unconditionally stable Crank-Nicolson algorithm with enhanced absorption for rotationally symmetric multi-scale problems in anisotropic magnetized plasma
Yi WEN, Junxiang WANG, Hongbing XU
Journal of Systems Engineering and Electronics    2024, 35 (1): 65-73.   DOI: 10.23919/JSEE.2023.000072
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Large calculation error can be formed by directly employing the conventional Yee’s grid to curve surfaces. In order to alleviate such condition, unconditionally stable Crank-Nicolson Douglas-Gunn (CNDG) algorithm with is proposed for rotationally symmetric multi-scale problems in anisotropic magnetized plasma. Within the CNDG algorithm, an alternative scheme for the simulation of anisotropic plasma is proposed in body-of-revolution domains. Convolutional perfectly matched layer (CPML) formulation is proposed to efficiently solve the open region problems. Numerical example is carried out for the illustration of effectiveness including the efficiency, resources, and absorption. Through the results, it can be concluded that the proposed scheme shows considerable performance during the simulation.

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Requirements ranking based on crowd-sourcing high-end product USs
Yufeng MA, Yajie DOU, Xiangqian XU, Qingyang JIA, Yuejin TAN
Journal of Systems Engineering and Electronics    2024, 35 (1): 94-104.   DOI: 10.23919/JSEE.2023.000164
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Based on the characteristics of high-end products, crowd-sourcing user stories can be seen as an effective means of gathering requirements, involving a large user base and generating a substantial amount of unstructured feedback. The key challenge lies in transforming abstract user needs into specific ones, requiring integration and analysis. Therefore, we propose a topic mining-based approach to categorize, summarize, and rank product requirements from user stories. Specifically, after determining the number of story categories based on pyLDAvis, we initially classify “I want to” phrases within user stories. Subsequently, classic topic models are applied to each category to generate their names, defining each post-classification user story category as a requirement. Furthermore, a weighted ranking function is devised to calculate the importance of each requirement. Finally, we validate the effectiveness and feasibility of the proposed method using 2 966 crowd-sourced user stories related to smart home systems.

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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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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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Anti-swarm UAV radar system based on detection data fusion
Pengfei WANG, Jinfeng HU, Wen HU, Weiguang WANG, Hao DONG
Journal of Systems Engineering and Electronics    2024, 35 (5): 1167-1176.   DOI: 10.23919/JSEE.2023.000077
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There is a growing body of research on the swarm unmanned aerial vehicle (UAV) in recent years, which has the characteristics of small, low speed, and low height as radar target. To confront the swarm UAV, the design of anti-UAV radar system based on multiple input multiple output (MIMO) is put forward, which can elevate the performance of resolution, angle accuracy, high data rate, and tracking flexibility for swarm UAV detection. Target resolution and detection are the core problem in detecting the swarm UAV. The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar. Since MIMO radar has better performance in resolution, swarm UAV detection still has difficulty in target detection. This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect. Subsequently, signal processing and data processing based on the detection fusion algorithm above are designed, forming a high resolution detection loop. Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

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Improved spatio-temporal alignment measurement method for hull deformation
Dongsheng XU, Yuanjin YU, Xiaoli ZHANG, Xiafu PENG
Journal of Systems Engineering and Electronics    2024, 35 (2): 485-494.   DOI: 10.23919/JSEE.2023.000139
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In this paper, an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle. Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time. The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatio-temporal aligned hull deformation measurement model. In addition, two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation. The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

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Online task planning method of anti-ship missile based on rolling optimization
Faxing LU, Qiuyang DAI, Guang YANG, Zhengrong JIA
Journal of Systems Engineering and Electronics    2024, 35 (3): 720-731.   DOI: 10.23919/JSEE.2024.000059
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Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment, online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.

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Research on FMC analytic algorithm of PBN track transition coding
Guangming ZHANG, Siqian REN, Weiwei ZHAO, Xiuyi LI
Journal of Systems Engineering and Electronics    2023, 34 (4): 1042-1052.   DOI: 10.23919/JSEE.2023.000090
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Airborne navigation database (NavDB) coding directly affects the result of analysis on the instrument flight procedure by the modern aircraft flight management computer (FMC). A reasonable flight track transition mode can improve the track tracking accuracy and flight quality of the aircraft. According to the path terminator (PT) and track transition characteristics of the performance based navigation (PBN) instrument flight procedure and by use of the world geodetic system (WGS)-84 ellipsoidal coordinate system, the algorithms for “fly by” and “fly over” track transition connections are developed, together with the algorithms for coordinates of fix-to-altitude (FA) altitude termination point and heading-to-an-intercept (VI) track entry point and for track transition display of the navigation display (ND). According to the simulation carried out based on the PBN instrument approach procedure coding of a certain airport and the PBN route data at a high altitude, the algorithm results are consistent with the FMC-calculated results and the actual ND results.

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Survivability model of LEO satellite constellation based on GERT with limited backup resources
Yuanyuan NIE, Zhigeng FANG, Sifeng LIU, Su GAO
Journal of Systems Engineering and Electronics    2024, 35 (4): 976-986.   DOI: 10.23919/JSEE.2024.000089
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Survivability is used to evaluate the ability of the satellite to complete the mission after failure, while the duration of maintaining performance is often ignored. An effective backup strategy can restore the constellation performance timely, and maintain good network communication performance in case of satellite failure. From the perspective of network utility, the low Earth orbit (LEO) satellite constellation survivable graphical evaluation and review technology (GERT) network with backup satellites is constructed. A network utility transfer function algorithm based on moment generating function and Mason formula is proposed, the network survivability evaluation models of on-orbit backup strategy and ground backup strategy are established. The survivable GERT model can deduce the expected maintenance time of LEO satellite constellation under different fault states and the network utility generated during the state maintenance period. The case analysis shows that the proposed survivable GERT model can consider the satellite failure rate, backup satellite replacement rate, maneuver control replacement ability and life requirement, and effectively determine the optimal survivable backup strategy for LEO satellite constellation with limited resources according to the expected network utility.

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