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Radar Signal Deinterleaving in Electronic Warfare Systems: A Combined Approach

IEEE ACCESS 2023年 11卷 142043-142061页

作者： Nuhoglu, Mustafa Atahan Cirpan, Hakan Ali ASELSAN AS Radar Elect Warfare & Intelligence Syst Div TR-06830 Ankara Turkiye Istanbul Tech Univ Elect & Commun Engn Dept TR-34396 Istanbul Turkiye

Traditional deinterleaving algorithms first cluster the pulses in frequency, pulse width, and angle of arrival parameter space. Then, they utilize time of arrival analysis techniques. This results in a performance loss, since these processes are not jointly combined;rather, they are sequentially connected. Additionally, they perform poorly in the presence of radars with staggered pulse repetition interval type due to abrupt time difference variations. To alleviate the suboptimality in the design and enhance the detection performance in the staggered type, we propose a combined approach based on the pulse repetition interval transform technique. It does not cluster the pulses as the traditional methods, instead, it combines all pulses by distance weighting. It deals with the detection problem in the staggered type by clustering the phases of the autocorrelation function. We develop an optimal weighting mechanism to fuse the outputs from phase clusters. Numerical results show that our method outperforms the traditional methods in different detection metrics via multiple scenarios, which is also validated by the theoretical findings.

关键词： Radar Transforms Clustering algorithms Receivers Radar detection Histograms Doppler radar Computational efficiency Electronic warfare Blind source separation clustering algorithms computational efficiency detection algorithms electronic countermeasures electronic warfare pattern clustering pattern recognition radar radar detection statistical analysis

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Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第10期31卷 3664-3678页

作者： Li, Mengqi Huang, Jing Xu, Yejun Liu, Xia Herrera-Viedma, Enrique Hohai Univ Business Sch Nanjing 211100 Peoples R China Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Granada 18071 Spain Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Univ Granada Dept Comp Sci & AI Andalusian Res Inst Data Sci & Computat Intelligen Granada 18071 Spain

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): first, information credibility directly determines the reliability of clustering results and final decision output;second, consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this article proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. First, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion, and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of "916" Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.

关键词： Social networking (online) Decision making Reliability Clustering algorithms Indexes Heuristic algorithms detection algorithms Clustering consensus credibility minority opinions social-network large-scale decision making (SN-LSDM)

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SR-POSE: A Novel Non-Contact Real-Time Rehabilitation Evaluation Method Using Lightweight Technology

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 4179-4188页

作者： Zhang, Kun Zhang, Pengcheng Tu, Xintao Liu, Zhicheng Xu, Peixia Wu, Chenggang Crookes, Danny Hua, Liang Nantong Univ Sch Elect Engn Nantong 226001 Jiangsu Peoples R China Nantong Key Lab Intelligent Control & Intelligent Nantong 226001 Jiangsu Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN North Ireland

Rehabilitation movement assessment often requires patients to wear expensive and inconvenient sensors or optical markers. To address this issue, we propose a non-contact and real-time approach using a lightweight pose detection algorithm-Sports Rehabilitation-Pose (SR-Pose), and a depth camera for accurate assessment of rehabilitation movement. Our approach utilizes an E-Shufflenet network to extract underlying features of the target, a RLE-Decoder module to directly regress the coordinate values of 16 key points, and a Weight Fusion Unit (WFU) module to output optimal human posture detection results. By combining the detected human pose information with depth information, we accurately calculate the angle between each joint in three-dimensional space. Furthermore, we apply the DTW algorithm to solve the distance measurement and matching problem of video sequences with different lengths in rehabilitation evaluation tasks. Experimental results show that our method can detect human joint nodes with an average detection speed of 14.32ms and an average detection accuracy for pose of 91.2%, demonstrating its computational efficiency and effectiveness for practical application. Our proposed approach provides a low-cost and user-friendly alternative to traditional sensor-based methods, making it a promising solution for rehabilitation movement assessment.

关键词： Feature extraction Sports Decoding Training detection algorithms Three-dimensional displays Pose estimation Rehabilitation exercise assessment SR-Pose E-Shufflenet RLE-Decoder DTW

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BurstSketch: Finding Bursts in Data Streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第11期35卷 11126-11140页

作者： Miao, Ruijie Zhong, Zheng Guo, Jiarui Li, Zikun Yang, Tong Cui, Bin Peking Univ Sch Comp Sci Beijing 100191 Peoples R China Peking Univ Natl Engn Lab Big Data Anal Technol & Applicat Beijing 100191 Peoples R China PCL Res Ctr Networks & Commun Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

Burst is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. To detect bursts accurately in real time, we propose a novel sketch, namely BurstSketch, which consists of two stages. Stage 1 uses the technique Running Track to select potential burst items efficiently. Stage 2 monitors the potential burst items and captures the key features of burst pattern by a technique called Snapshotting. We further propose an optimization, namely Dynamic Buckets, which can improve the accuracy of BurstSketch. We provide theoretical error bounds for Stage 1, Stage 2 and the optimized version. Experimental results show that, compared with the strawman solution, Burstsketch achieves 2.00 to 11.63 times higher F1 score, and 1.56 times higher throughput. We also integrate BurstSketch into Apache Flink, and show that using BurstSketch can be faster than simply using the built-in APIs provided by Apache Flink.

关键词： Real-time systems Memory management Time-frequency analysis Throughput detection algorithms Data structures Symbols Approximate query burst data stream sketch

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Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

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IEEE ACCESS 2023年 11卷 13690-13703页

作者： Tian, Chengjun Liu, Haobo Liu, Zhe Li, Hongyang Wang, Yuyu Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy

关键词： Simultaneous localization and mapping Robots Laser radar Laser fusion detection algorithms Measurement by laser beam Degradation Laser SLAM Gmapping algorithm multi-sensor fusion SLAM system loop closure detection

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Mining Statistically Significant Communities From Weighted Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第6期35卷 6073-6084页

作者： He, Zengyou Chen, Wenfang Wei, Xiaoqi Liu, Yan Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116024 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

As one of the most important issues in data mining and network science, the community detection problem has been extensively investigated during the past decades. Despite of the success achieved by existing methods, how to directly access the statistical significance of an individual community in a weighted network remains unsolved. To address this issue, we present a new method to calculate the analytical p-value of an individual community in weighted networks. The proposed analytical p-value is able to assess the statistical significance that one target community appears in a random weighted graph in a straightforward manner. To verify the effectiveness of the proposed p-value in community evaluation, it is utilized as the objective function in a local search procedure to derive a new community detection algorithm. Experimental results show that the new algorithm is able to achieve comparable performance to those state-of-the-art algorithms for identifying communities from weighted networks.

关键词： Probability Measurement detection algorithms Software Machine learning algorithms Linear programming Image edge detection Community detection random graphs statistical significance weighted networks

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Lossdetection: Real-Time Packet Loss Monitoring System for Sampled Traffic Data

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 30-45页

作者： Wu, Hua Liu, Ya Ni, Shanshan Cheng, Guang Hu, Xiaoyan Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Nanjing 210096 Peoples R China Purple Mt Labs Network & Commun Secur Nanjing 211111 Peoples R China Jiangsu Prov Engn Res Ctr Secur Ubiquitous Network Nanjing 211102 Peoples R China

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called Lossdetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that Lossdetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.

关键词： Packet loss Real-time systems Feature extraction Monitoring Systematics detection algorithms Current measurement Packet loss detection sketch real-time detection machine learning

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Algorithm of detection and Tracking for Laboratory Unsafe Behavior Based on Computer Vision

Algorithm of Detection and Tracking for Laboratory Unsafe Be...

引用

2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Gao, Lu Bohai University College of Physical Science and Technology Jinzhou China

ISBN: (纸本)9798350313413

Computer vision gives computers the ability of visual perception similar to people, so that computers can feel the environment in the field of vision, understand the content of the feeling, and take corresponding actions. Object tracking is a problem of creating corresponding matching between successive image frames based on position, speed, shape, texture and color. Many security problems in laboratories are caused by unsafe behaviors, and most laboratories have built a complete video surveillance system based on computer vision, using these video surveillance data to identify unsafe behaviors. In this paper, image denoising based on morphological filter algorithm, moving target detection based on optical flow algorithm and moving target tracking based on Mean-Shift algorithm are used to provide key technical solutions for the study of unsafe behaviors in laboratories. The results show that the optical flow algorithm has good adaptability and is suitable for both static and dynamic background. If a more accurate similarity measurement function is used, the tracking time is shorter and the speed is faster. © 2023 IEEE.

关键词： computer vision detection algorithms Mean-Shift algorithm morphological filtering optical flow algorithm tracking algorithms unsafe behavior

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A New Index Modulation for LoRa

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IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11938-11939页

作者： Kang, Jae-Mo Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Long range (LoRa) is a widely adopted Internet of Things (IoT) technique, but its fatal limit is a low-data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both the coherent and noncoherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

关键词： Chirp Modulation Indexes Signal to noise ratio detection algorithms Symbols Sensitivity Chirp spread spectrum index modulation Internet of Things (IoT) long-range (LoRa)

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Broad Learning System-Based Detector for OFDM With Index Modulation

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IEEE COMMUNICATIONS LETTERS 2023年第1期27卷 224-228页

作者： Du, Ruiyan Wang, Shiyi Liu, Fulai Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

This letter presents a broad learning system (BLS) based orthogonal frequency division multiplexing with index modulation (OFDM-IM) detector called BLS-IM, which achieves bit-error-rate performance improvement with less training time. In BLS-IM, in order to help the network detect the index of the active subcarrier, the received data needs to be preprocessed into polar coordinates first. The processed data is then fed into the BLS network. In particular, the BLS is built in the form of a flat network where the initial signal bits are transferred among the feature nodes. At the same time, the structure is extended in a broad sense in terms of enhanced nodes. The proposed BLS-IM detector has an advantage in training time because it avoids the time-consuming training process caused by a large number of connection parameters in the layers. Simulation results demonstrate that BLS-IM is capable of achieving better detection performance with less training time than the existing algorithms.

关键词： Detectors Feature extraction Training Symbols Indexes Learning systems detection algorithms Broad learning system incremental learning OFDM-IM activated subcarriers index modulation detection

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