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基于壓縮感知理論的雷達(dá)成像技術(shù)與應(yīng)用研究進(jìn)展

李少東,  楊軍,  陳文峰,  馬曉巖

李少東, 楊軍, 陳文峰, 馬曉巖. 基于壓縮感知理論的雷達(dá)成像技術(shù)與應(yīng)用研究進(jìn)展[J]. 電子與信息學(xué)報, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
引用本文: 李少東, 楊軍, 陳文峰, 馬曉巖. 基于壓縮感知理論的雷達(dá)成像技術(shù)與應(yīng)用研究進(jìn)展[J]. 電子與信息學(xué)報, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
LI Shaodong, YANG Jun, CHEN Wenfeng, MA Xiaoyan. Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory[J]. Journal of Electronics & Information Technology, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
Citation: LI Shaodong, YANG Jun, CHEN Wenfeng, MA Xiaoyan. Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory[J]. Journal of Electronics & Information Technology, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874

基于壓縮感知理論的雷達(dá)成像技術(shù)與應(yīng)用研究進(jìn)展

doi: 10.11999/JEIT150874

Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory

  • 摘要: 壓縮感知理論基于信號稀疏性，將對信號采樣轉(zhuǎn)換為對信息自由度的采樣，可大大降低采樣率。而將壓縮感知理論應(yīng)用于雷達(dá)成像時有望在以下幾個方面得到改善：增強(qiáng)成像性能，簡化雷達(dá)硬件設(shè)計，縮短數(shù)據(jù)獲取時間，減少數(shù)據(jù)量和傳輸量等。該文從壓縮感知的稀疏性，壓縮采樣，無模糊重建3個關(guān)鍵步驟與成像雷達(dá)有機(jī)結(jié)合的角度，對近年來基于壓縮感知理論的雷達(dá)成像技術(shù)研究現(xiàn)狀進(jìn)行系統(tǒng)綜述，重點(diǎn)論述場景稀疏性與成像關(guān)系, 壓縮采樣方法(包括硬件)設(shè)計，場景圖像快速高精度重建以及成像系統(tǒng)體制應(yīng)用等方面，最后探討了壓縮感知理論應(yīng)用尚需解決的問題和進(jìn)一步發(fā)展方向。
    Abstract: Compressive Sensing (CS) theory, based on the sparsity of interested signal, samples degree-of-freedom of signal. CS is expected to improve the performance of imaging radar in the following aspects: improving the quality of imaging, simplifying the designing of radar hardware, shortening the imaging time and compressing data. This paper first combines the analysis of radar imaging with the three aspects of CS, namely the sparsity of interested signal, the compressive sampling and optimization method. Thereafter a particular and comprehensive review of CS theory in imaging radar is summarized, mainly including the relationship between sparsity of the scene and imaging, compressive sampling methods, fast and accurate reconstruction of the scene and the applications to different imaging radar systems. Finally, the unresolved problems in current research and further study directions are pointed out.
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  • 收稿日期:  2015-07-21
  • 修回日期:  2015-12-08
  • 刊出日期:  2016-02-19

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