| 基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究 |
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| 引用本文： | 潘俊,叶盛波,史城,倪志康,郑之杰,方广有. 基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究[J]. 电子与信息学报, 2022, 44(4): 1241-1248. DOI: 10.11999/JEIT211042 |
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| 作者姓名： | 潘俊 叶盛波 史城 倪志康 郑之杰 方广有 |
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| 作者单位： | 1.中国科学院空天信息创新研究院 北京 1000942.中国科学院电磁辐射与探测技术重点实验室 北京 1000943.中国科学院大学电子电气与通信工程学院 北京 100049 |
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| 基金项目： | 国家自然科学基金;科技部重点研发计划 |
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| 摘 要： | 废墟下呼吸信号的检测对地震救援具有重要意义.在实际中,障碍物(如墙体)后的人体呼吸信号会被环境中的噪声所掩盖.如何提升穿墙呼吸信号的信噪比(SNR)仍是一项具有挑战性的工作.该文提出一种基于先验信噪比估计的检测算法,用于增强穿墙弱呼吸信号的输出SNR.该算法在谱减法中典型的决策导向(DD)算法基础上加入了自适应权重因子...
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| 关 键 词： | 穿墙雷达 超宽带 呼吸检测 先验信噪比 |
| 收稿时间： | 2021-09-27 |
Study on Respiration Signal Detection Algorithm of Ultra-WideBand Through-wall Radar Based on A Priori Signal-to-Noise Ratio Estimation |
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| PAN Jun,YE Shengbo,SHI Cheng,NI Zhikang,ZHENG Zhijie,FANG Guangyou. Study on Respiration Signal Detection Algorithm of Ultra-WideBand Through-wall Radar Based on A Priori Signal-to-Noise Ratio Estimation[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1241-1248. DOI: 10.11999/JEIT211042 |
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| Authors: | PAN Jun YE Shengbo SHI Cheng NI Zhikang ZHENG Zhijie FANG Guangyou |
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| Affiliation: | 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China2.Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100094, China3.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | The detection of respiration signal under the ruins is of great significance to earthquake rescue. In reality, the human respiration signal behind the obstacle (such as walls) will be masked by noise in the environment. How to improve the Signal-to-Noise Ratio (SNR) of the through-wall respiration signal is still a challenging task. A detection algorithm based on a priori SNR estimation for enhancing the output SNR of the weak through-wall respiration signal is proposed in this paper. Based on the typical Decision-Directed (DD) algorithm of spectral subtraction methods, an adaptive weighting factor is added in the proposed algorithm to eliminate further the residual random noise by reducing the estimation error of the a priori SNR. The performance of the proposed algorithm is investigated through simulation and experimental verification. The output SNR of the proposed respiration detection algorithm is improved compared with the traditional Fast Fourier Transform (FFT), Singular Value Decomposition (SVD), and DD detection algorithm. |
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