| 基于压缩感知的气象雷达回波压缩采样与重建 |
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| 引用本文： | 刘露,何建新,曾强宇.基于压缩感知的气象雷达回波压缩采样与重建[J].成都信息工程学院学报,2014,29(5):503-508. |
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| 作者姓名： | 刘露 何建新 曾强宇 |
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| 作者单位： | 成都信息工程学院电子工程学院,四川成都,610225 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 由于Nyquist采样定理的限制,高分辨率气象雷达面临采样率过高、数据存储量过大等问题。压缩感知理论可以实现气象雷达信号的压缩采样,解决采样率过高等问题。基于压缩感知理论,分析了气象雷达回波信号的稀疏性,建立了气象雷达回波信号的压缩采样和重建的过程,并结合气象雷达实测的回波数据进行仿真。仿真结果表明,0.3倍采样率下的重建回波与原始回波存在较大误差,0.5倍采样率时误差明显降低,0.7倍采样率时则可高概率重建出原始回波。因此,将压缩感知理论应用于高分辨率气象雷达的信号处理中,可以实现在较低采样率下高概率重建原始回波信号。
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| 关 键 词： | 气象雷达 压缩感知 稀疏分解 非相干测量 小波变换 OMP算法 |
Compression Sampling and Reconstruction of Meteorological Radar Echo based on Compressive Sensing |
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| LIU Lu,HE Jian-xin,ZENG Qiang-yu.Compression Sampling and Reconstruction of Meteorological Radar Echo based on Compressive Sensing[J].Journal of Chengdu University of Information Technology,2014,29(5):503-508. |
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| Authors: | LIU Lu HE Jian-xin ZENG Qiang-yu |
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| Institution: | 1.College of Electornic Engineering, Chengdu University of Information Technology, Chengdu 610225, China) |
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| Abstract: | Because of the restriction of Nyquist sampling theorem,the high resolution meteorological radars face some problems such as unmatched high sampling frequency and huge mass of data.Compressive sampling of the radar signal can be achieved by compressive sensing theorem and some problems such as the high sampling frequency can be solved by this theorem too.Based on compressive sensing theorem,this paper has analyzed the sparsity of weather radar signals,established the compressive sampling and reconstruction process of meteorological radar echo signals,and combined with the actual measurement data of the meteorological radar to simulate that process.Simulation result shows that error between raw echo and reconstruction under 0.3 times sample rate is lager,and error under 0.5 times sample rate is decreased obviously,and under 0.7 times sample rate the raw echo can be reconstructed with high probability.Therefore under the low sampling rate original echo can be reconstructed by the signal processing which applied with the compressive sampling theorem. |
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| Keywords: | weather radar compressive sensing sparse decomposition incoherent measurement wavelet transform OMP algorithm |
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