2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)最新文献_第6页

Moving Target Detection and Speed Estimation for One-stationary Bistatic Synthetic Aperture Radar Based on Multi-channels 基于多通道的单稳态双基地合成孔径雷达运动目标检测与速度估计

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984525

Hongtu Xie, Xiao Hu, Jiaxing Chen, Zhitao Wu, Guoqian Wang

{"title":"Moving Target Detection and Speed Estimation for One-stationary Bistatic Synthetic Aperture Radar Based on Multi-channels","authors":"Hongtu Xie, Xiao Hu, Jiaxing Chen, Zhitao Wu, Guoqian Wang","doi":"10.1109/ICSPCC55723.2022.9984525","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984525","url":null,"abstract":"One-stationary bistatic synthetic aperture radar (OS BSAR) has the advantages of the high-resolution imaging, added target information, low cost, high security, and forward-looking imaging. However, due to the stationary platform in the OS BSAR system, the existing moving target detection and speed estimation methods based on the multi-channel can’t be directly applied to the OS BSAR system. In this paper, the moving target detection and speed estimation for the OS BSAR system based on the multi-channels is proposed. According to OS BSAR geometry and the echo signal characteristics, a moving target detection method based on the multi-channels has been proposed, which can effectively suppress the clutter of the stationary targets and preserve the echo information of the moving targets. For the speed estimation of moving targets, the distance speed estimation method using the distance walking and then the azimuth speed estimation method using the moving target image azimuth offset are studied respectively, which are simple to implement and can effectively estimate the moving target speed. The simulation experiments prove the effectiveness of the proposed method.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Spatio-Temporal Interactive Attention Network for Motor Imagery EEG Decoding 运动意象脑电解码的时空交互注意网络

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984387

Yue Ma, Doudou Bian, Dongyang Xu, W. Zou, Jiajun Wang, Nan Hu

{"title":"A Spatio-Temporal Interactive Attention Network for Motor Imagery EEG Decoding","authors":"Yue Ma, Doudou Bian, Dongyang Xu, W. Zou, Jiajun Wang, Nan Hu","doi":"10.1109/ICSPCC55723.2022.9984387","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984387","url":null,"abstract":"Brain-computer interface (BCI) technology can link direct communication paths between human brain and external devices, where tasks of motor imagery (MI) electroencephalogram (EEG) decoding play important roles. Multi-channel electrode montage achieves EEG measurements with high spatial resolution. In previous studies of MI-EEG decoding, the extracted temporal features of multi-channel EEG measurement data were harnessed to recognize different MI-EEG patterns, while spatial features, especially those manifesting the intrinsic connectivity of EEG channels during different MI tasks, has often been overlooked. In this paper, we propose a spatio-temporal interactive attention network (STIA-Net), which exploits spatial features, temporal features, as well as their interaction, for MI-EEG decoding. Graph convolution is employed for spatial feature manipulation, where functional connectivity with phase locking value (PLV) is involved to establish a graph and hence exhibiting topological structural properties. The temporal features are extracted by dilated temporal convolutions, and spatio-temporal interaction is accomplished via attention mechanism. The STIA-Net utilizes the spatio-temporal feature fusion for ultimate MI-EEG classification. The experimental results demonstrate that the proposed STIA-Net performs well on the PhysioNet MI-EEG dataset, with a subject-independent classification accuracy of 83.9%, higher than state-of-the-art methods.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115346741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Speech Enhancement Based on Multi-Objective Ensemble Learning 基于多目标集成学习的语音增强

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984412

Yonglin Wu, Jun Zhang, Yue Wu, Geng-xin Ning, Cui Yang

引用次数: 0

Research on adaptive matched filtering algorithm of active sonar echo signal 主动声纳回波信号自适应匹配滤波算法研究

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984241

Kaiju Wang, Haoquan Guo, Xiangling Meng, Fuzhao Chu

{"title":"Research on adaptive matched filtering algorithm of active sonar echo signal","authors":"Kaiju Wang, Haoquan Guo, Xiangling Meng, Fuzhao Chu","doi":"10.1109/ICSPCC55723.2022.9984241","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984241","url":null,"abstract":"Matched filter is the best detector of active sonar in the background of Gaussian white noise, but its detection performance is limited, and often can not meet the detection requirements under the condition of lower signal-to-noise ratio (SNR). In order to detect the echo signal under the condition of lower signal-to-noise ratio, an adaptive matched filter algorithm based on conventional matched filter, t0 filter, correlation denoising and adaptive line spectrum enhancement processing is proposed in this paper. The matched filter output signal is first processed by t0 filtering, then by correlation denoising and adaptive line spectrum enhancement. After triple processing, the echo signal and interference can be separated under the background of low SNR, which significantly improves the output SNR. The simulation results show that the algorithm proposed in this paper can significantly improve the echo detection ability under the condition of weak SNR, and can detect the -20db echo signal after beamforming, and the performance is significantly better than the traditional matched filter.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132457027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Radar Target Recognition based on Transferring Meta Knowledge 基于元知识传递的少射雷达目标识别

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984385

Yuan Yan, Jingming Sun, Junpeng Yu, Yuhao Yang, Lin Jin

{"title":"Few-Shot Radar Target Recognition based on Transferring Meta Knowledge","authors":"Yuan Yan, Jingming Sun, Junpeng Yu, Yuhao Yang, Lin Jin","doi":"10.1109/ICSPCC55723.2022.9984385","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984385","url":null,"abstract":"Aiming at new types of few shot enemy targets in the combat scenario, a few shot radar target recognition technology based on transferring meta knowledge is proposed. This technology simulates the human learning process. First, a learning mechanism for multiple recognition tasks is built. Secondly, By learning recognition process of different tasks, the ability such as quickly adaption, strengthen generalization is gained, meta knowledge is accumulated gradually. Finally, the meta knowledge is transferred to support the model to achieve fast and accurate learning in the new few-shot recognition scenario. The proposed algorithm can realize the fast and accurate recognition of new types of radar targets in few shot scenarios (5 samples or less). Good results have been achieved in the full-angle field measured data set of a HRRP dataset and the public MSTAR dataset. It has a recognition rate of 97.4% when only 5 samples of new types of targets are learned in the MSTAR dataset. The optimal recognition rate based on HRRP dataset is 79.1%. Under the condition that only one sample is learned for each new type of target, the average recognition accuracy of three classifications can reach 64.1%. At the same time, the algorithm can overcome the angle sensitivity of radar data to a certain extent, which is very utility in practical scenarios.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131564182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Maximum Mean Discrepancy Adversarial Active Learning 最大平均差异对抗性主动学习

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984505

Mingzhi Cai, Baoguo Wei, Yuechen Zhang, Xu Li, Lixin Li

引用次数: 0

Full Pulse Data Generation Method for SAR Signal Based on Antenna Direction Diagram and Motion Model 基于天线方向图和运动模型的SAR信号全脉冲数据生成方法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984243

Qiyong Liu, Yichang Chen, Qingyun Wei, Xin Wang

引用次数: 0

A Novel Multi-Dimensional Feature Extraction Framework of Data Sampled by Electronic Nose 一种新的电子鼻采样数据多维特征提取框架

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984388

Mingqi Jin, Wentao Shi, Haoyue Fu, Zewen Li

引用次数: 0

Low-Complexity Multidimensional MUSIC Algorithm Incorporating Across-Neighborhood Search Mechanism 基于跨邻域搜索机制的低复杂度多维MUSIC算法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984337

Yameng Jiao, Wenping Li, Lin Cui

引用次数: 0

Performance analysis of a new two-stage tunable detector 一种新型两级可调谐探测器的性能分析

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984576

Dongsheng Zhu, Da Xu, Xiaojing Su

引用次数: 0