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2021 CIE International Conference on Radar, Radar 2021

作者： Chen, Bowei Wang, Yuqi Sun, Guang-Cai Xing, Mengdao Zhang, Zijing Yang, Xiaoniu Xidian University National Laboratory of Radar Signal Processing Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665498142

A long synthetic aperture passive localization algorithm is proposed in this paper. First of all, the Doppler change rate of signal can be measured by the image contrast and then, grid points are divided within the maximum possible latitude and longitude of the emitter location, the Doppler change rate at each grid point can be calculated by using known position and velocity of the receiver. The grid points at which the calculated Doppler change rate is equal to the value measured by the received signal form an isoline. Two receivers can draw two isolines on the ground and the intersection of the isolines gives estimate of the emitter location. © 2021 IEEE.

关键词： Azimuth focusing Doppler change rate Passive localization Synthetic aperture localization

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Mitigation of Non-stationary Jammings with Missing Samples for GNSS Using Sparse Representation

Mitigation of Non-stationary Jammings with Missing Samples f...

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2022 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Ren, Yuetao Zhi, Yongfeng Zhang, Jun School of Automation Northwestern Polytechnical University Xi'an710072 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

Global navigation satellite systems (GNSS) are widely used in most civil and military applications. However, GNSS receivers suffer from severe performance degradation when jammed by electromagnetic interference. This paper considers the mitigation problem of non-stationary jammings with missing samples for GNSS. We propose to reconstruct the interference by sparse representation utilizing the sparsity of jammings in the time-frequency (TF) domain. The GNSS signal is recovered by eliminating the reconstructed interference from the received signal. First, the instantaneous frequencies (IFs) of jammings are extracted from adaptive directional TF distributions. Then, we construct the over-complete atomic dictionary based on the estimated IFs. The interference is reconstructed by orthogonal matching pursuit (OMP) algorithm. The proposed method can achieve the mitigation of non-stationary jamming and the acquisition of GNSS signals in the presence of missing samples. We confirm the effectiveness of the algorithm by simulations. © 2022 IEEE.

关键词： Jamming

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Interference Utilization with Bandwidth Allocation in DFRC Systems

Interference Utilization with Bandwidth Allocation in DFRC S...

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International Conference on Computers, communications, and Systems (ICCCS)

作者： Zhan Xu Shengfeng Kang Xiaolong Yang Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Dual-functional Radar and communication (DFRC) has become one of the key technologies of advanced wire-less systems. This is because of the continuous development of mobile communication, the scarcity of spectrum and the potential network applications. In this paper, we propose a symbol-level precoding optimization scheme for Multi-Input-Multi-Output (MIMO) radar and multi-user Multiple-Input-Single-Output (MU-MISO) communication systems. More specif-ically, the weight sum of communication rate and radar detection probability are maximized by jointly optimizing the precoding vector and bandwidth allocation. Meanwhile, it includes the condition of ensuring the receiving signal-to-interference-plus-noise ratio (SINR) at receivers, the interference to the radar and the transmission power budget at the base station (BS). Com-pared with the traditional beamforming based on Semidefinite Relaxation (SDR), the feasible area of the optimization problem is extended. Based on the concept of constructive interference, the harmful multi-user interference (MUI) is converted into a beneficial signal. So the communication rate is ensured and the system performance is improved. Numerical simulations show that our proposed method achieves a better result of the object function compared to benchmark methods, while obtaining a favorable bandwidth allocation.

关键词： Base stations Precoding System performance Radar detection Interference Radar Channel allocation

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Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

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International Journal of Machine Learning and Cybernetics 2025年第5期16卷 3759-3760页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5College of control science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6School of information and security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2School of information and security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6College of control science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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A Mixed Intrusion Detection System utilizing K-means and Extreme Gradient Boosting 2

A Mixed Intrusion Detection System utilizing K-means and Ext...

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2022 2nd International Conference on communication technology and information technology, ICCTIT 2022

作者： Lv, Haifeng Ji, Xiaoyu Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed model called K-means-XGBoost consisting of K-means and (Extreme Gradient Boosting, XGBoost) algorithms. The distributed computation of our method is achieved in Spark platform to rapidly separate normal events and anomaly events. In phrases of accuracy, DR, F1-score, recall, precision, and error indices FAR, the proposed model's performance is measured via the well-known dataset of NSL-KDD. The experimental outcomes indicate that our method is outstandingly better among accuracy, DR, F1-score, training time, and processing speed, compared to other models which are recently created. In particular, the accuracy, F1-score, and DR of the proposed model can achieve as high as 93.28%, 94.39%, and 99.22% in the NSL-KDD dataset, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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An Anti-jamming Intelligent Decision-Making Method for Multi-user communication Based on Deep Reinforcement Learning 22

An Anti-jamming Intelligent Decision-Making Method for Multi...

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22nd IEEE International Conference on communication technology, ICCT 2022

作者： Zhang, Lan Ma, Liang Tian, Feng Liang, Tianyi Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China The 36th Research Institution of China Electronics Technology Group Corporation Jiaxing341000 China Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9781665470674

In order to effectively cope with the external malicious jamming from jammers and avoid mutual interference caused by competitive channels among users, an intelligent antijamming decision-making method based on deep reinforcement learning for multi-user communication is proposed in this paper. In multiple jamming modes, the base station perceives the current spectrum information of the multi-user and the jammer and makes it as the input of the deep reinforcement learning strategy neural network, then selects the joint action according to the dynamic greedy algorithm, which will help the users intelligently select the communication frequency band via the feedback from the base station. The simulation results show that the proposed method effectively overcome the communication jamming induced by external jammers and internal users. Moreover, the normalized throughput of each user in the proposed algorithm is much higher than the random frequency hopping and fixed ϵ deep reinforcement learning algorithm with faster convergence speed. © 2022 IEEE.

关键词： Reinforcement learning

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Fast Informer-Based Time Series Detection

Fast Informer-Based Time Series Detection

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Weiguo Shen Tianhao Xia Dongwei Xu Qi Xuan Yun Lin Wei Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China Zhejiang University of Technology College of Information Engineering Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China Zhejiang University of Technology Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China College of Information and Communication Engineering Harbin Engineering University Binjiang Institute of Artificial Intelligence Harbin China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

Time series data are pivotal in modern technology and industrial applications,but they are frequently impacted by anomalies due to system failures,cyberattacks,or unexpected events. Therefore,swift and accurate anomaly detection in time series is crucial. This paper introduces ‘TransIDS’,an intrusion detection method that focuses on the efficient extraction and detection of time series features. TransIDS uses the Informer model to capture temporal features within data frames more effectively,improving detection performance and reducing runtime. We also fine-tune the Informer model and introduce an automatic anomaly threshold selection method,optimizing it for anomaly detection tasks. We validate TransIDS through experiments on various datasets and compare it with three existing methods. The results show that TransIDS outperforms the compared models in detection performance.

关键词： Runtime Accuracy Antenna theory Time series analysis Intrusion detection Predictive models Feature extraction Transformers Data models Anomaly detection

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Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based on Light Gradient Boosting Model

Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based o...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yanfang Lv Lanjie Zhang Yifan Zhang Biao Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

As an essential component of the earth system, precipitation plays a crucial role in the regional and global water cycle. This paper uses observations from the Fengyun-3D microwave humidity and temperature sounder and microwave radiation imager, as well as Integrated Multi-satellite Retrievals for GPM (IMERG), to establish a precipitation retrieval model based on the light gradient boosting model (LGBM). Using IMERG as the comparison data, the performance of precipitation identification with the LGBM-based precipitation retrieval model is as follows: accuracy is 0.9302, probability of detection is 0.7683, false alarm rate is 0.3529, and critical success index is 0.5414. The performance of precipitation estimation is also evaluated, with a correlation coefficient of 0.7536, a mean absolute error of 0.9119, and a mean squared error of 3.9506. In summary, this study achieves good performance in precipitation identification and estimation by the LGBM model.

关键词： Earth Correlation coefficient Precipitation Estimation Humidity Boosting Data models

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer Based on LightGBM

Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Xuchen Dai Biao Zhang Yifan Zhang Lanjie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) passive microwave radiometer under the severe weather such as typhoon is evaluated. Based on the observation data of HY-2C, this study explores the influence of brightness temperature (TB) observed by microwave radiometer on sea surface wind speed retrieval under typhoon conditions. In the case of typhoon precipitation, the attenuation of the atmosphere to the high frequency channel is stronger, which makes the sea surface information observed by the microwave radiometer very weak. Therefore, we mainly select the TB of horizontal polarization and vertical polarization observation of 6.9GHz and 10.7GHz channels to carry out the study of typhoon wind speed retrieval. This paper constructs a matching data set based on the TB observed by HY-2C passive microwave radiometer, HY-2C L2B wind product and H*wind reanalysis wind field data in October and November 2020. The maximum differences of spatial and temporal were set as 25km and 30minutes, respectively. Then, the typhoon wind speed retrieval model based on LightGBM is established. We use the data of October 2020 to train the model and the data of November 2020 to test model performance. Comparing with the H*wind reanalysis wind field data, the bias and root-mean-square error of retrieved wind speed are 0.15m/s and 1.26m/s, respectively. In addition, the correlation coefficient is 0.90, indicating that the retrieved wind speed has a good consistency with H*wind data.

关键词： Microwave measurement Sea surface Tropical cyclones Wind speed Microwave theory and techniques Data models Brightness temperature

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