压缩感知理论在雷达成像中的应用研究
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摘要
以ISAR和InISAR为代表的高分辨率雷达成像技术在军事和民用领域有着广泛的需求。通常情况下,高分辨率雷达图像的获得需要宽带雷达信号,而宽带雷达信号则又会导致雷达数据率的增加。近年来在雷达技术领域得到高度关注的压缩感知理论,其非相关测量过程能够有效地降低高分辨率雷达成像系统的数据率,有望解决雷达系统中超大数据量的采集、存储与传输问题。因此压缩感知理论和技术在雷达成像领域的应用,有可能会为高分辨率雷达成像技术带来巨大变革。压缩感知在高分辨率雷达成像中的应用研究工作虽然取得了一定的进展,但还没有针对压缩感知雷达成像理论进行系统性研究,也没能在此基础上给出实用化的成像算法。论文以基于压缩感知的雷达成像理论与算法作为研究内容,将压缩感知理论应用到高分辨率雷达成像算法中。论文围绕着成像数据获取方法、成像信号处理方法和压缩感知在宽带雷达成像中的应用等紧密联系而侧重不同的三个方面展开了研究,建立了匹配滤波体制和去斜体制下的基带回波信号稀疏表示模型,提出了压缩感知测量器应用到雷达接收机的数字方案与模拟方案,构建了具有保相性的压缩感知距离压缩算法,通过距离-方位解耦合的雷达成像框架,将压缩感知距离压缩算法与传统的雷达二维成像和InISAR三维成像算法相结合,形成了压缩感知雷达成像算法,并将其应用到调频步进宽带雷达成像中。论文通过对仿真和实测数据的处理,证明了所提出的方法的有效性。
论文的主要贡献体现在以下三个方面:
在基于压缩感知的雷达数据获取方法研究中,通过对雷达回波信号的分析,建立了匹配滤波体制和去斜体制下的雷达回波信号稀疏表示模型,并将模拟/信息转换器引入压缩感知雷达成像处理中,以实现对距离向雷达回波信号的实时测量。在此基础上提出了压缩感知测量器应用到雷达接收端的数字实现方案与模拟实现方案。
在压缩感知雷达成像算法研究中,首先在常用的稀疏信号重建算法中筛选出适合雷达成像的算法,然后与雷达回波信号稀疏表示模型以及非相干测量矩阵一起构建了具有保相性的压缩感知距离压缩算法。在此基础上利用距离-方位解耦合的雷达成像框架,将压缩感知距离压缩算法与传统的雷达二维成像和InISAR三维成像算法相结合,形成了压缩感知雷达成像算法。
在压缩感知宽带雷达成像算法研究中,结合调频步进信号的子脉冲合成方法,提出了针对调频步进信号的压缩感知测量方法,实现了压缩感知宽带雷达成像。
High-resolution radar imaging is widely demanded in many applications. Usually high-resolution radar imaging needs wide-band radar signals, and wide-band radar signals result in the increase of data rate. In recent years, compressive sensing (CS) theory is highly focused in radar community, and its incoherence measurement process can effectively reduce the data rate of high-resolution imaging radar system, and release the burden of radar system on huge amount of data sampling, storage and transmission. So, CS theory and technologies may bring deep change to high-resolution imaging radar system. Although the research of CS based radar imaging has made some progress, there is still lack of systemic research on the CS based radar imaging theory, and no practicable imaging algorithm. In the dissertation, the theory and algorithms of CS based radar imaging is discussed and applied to high-resolution radar imaging. The major works include the following three parts: the CS based radar imaging data acquisition methods, the CS based radar imaging algorithms and the application of CS in wide-band radar imaging. Firstly, we establish the sparse representation models of the baseband echo under both matched filtering and de-chirp processing, and propose digital or analog realization scheme of analog-to-information convertor in radar receiver. Secondly, we realize a phase-reservation CS based range compression algorithm, constructe a CS based radar imaging framework with range and azimuth decoupled and apply it to both 2D and 3D radar imaging combined with conventional imaging algorithms. Finally, we apply the CS based imaging method to wideband radar imaging system. The effectiveness of the proposed algorithms are tested through processing both simulation and real data.
The major contributions of the dissertation are summarized as follows:
In the study of CS based radar data acquisition methods, we firstly analyze the radar echo signal, and then establishe the sparse representation models of the processed signals under matching filter mode and de-chirp mode. Aiming to real-time measurement, we introduce Analog-to-Information converter(AIC) into compressive sensing imaging processing, and proposes both digital and analog solutions of AIC in radar receiver.
In the study of CS based imaging algorithms, we firstly select the sparse signal reconstruction algorithm suitable for radar imaging, and then propose a phase-reserve CS range compression algorithm combined with sparse representation of radar echo signal and non-correlation measurement matrix. Finally, we propose a range-azimuth decoupling radar imaging frame, in which CS range compression algorithm is combined with traditional radar 2D imaging and 3D imaging algorithms so as to realize the CS imaging algorithm.
In the study of CS based wide-band radar imaging algorithm, we propose a CS measurement method for stepped-frequency chirp signal (SFCS) and realize CS imaging for wide-band radar with application of the subaperture processing method of SFCS.
论文的主要贡献体现在以下三个方面:
在基于压缩感知的雷达数据获取方法研究中,通过对雷达回波信号的分析,建立了匹配滤波体制和去斜体制下的雷达回波信号稀疏表示模型,并将模拟/信息转换器引入压缩感知雷达成像处理中,以实现对距离向雷达回波信号的实时测量。在此基础上提出了压缩感知测量器应用到雷达接收端的数字实现方案与模拟实现方案。
在压缩感知雷达成像算法研究中,首先在常用的稀疏信号重建算法中筛选出适合雷达成像的算法,然后与雷达回波信号稀疏表示模型以及非相干测量矩阵一起构建了具有保相性的压缩感知距离压缩算法。在此基础上利用距离-方位解耦合的雷达成像框架,将压缩感知距离压缩算法与传统的雷达二维成像和InISAR三维成像算法相结合,形成了压缩感知雷达成像算法。
在压缩感知宽带雷达成像算法研究中,结合调频步进信号的子脉冲合成方法,提出了针对调频步进信号的压缩感知测量方法,实现了压缩感知宽带雷达成像。
High-resolution radar imaging is widely demanded in many applications. Usually high-resolution radar imaging needs wide-band radar signals, and wide-band radar signals result in the increase of data rate. In recent years, compressive sensing (CS) theory is highly focused in radar community, and its incoherence measurement process can effectively reduce the data rate of high-resolution imaging radar system, and release the burden of radar system on huge amount of data sampling, storage and transmission. So, CS theory and technologies may bring deep change to high-resolution imaging radar system. Although the research of CS based radar imaging has made some progress, there is still lack of systemic research on the CS based radar imaging theory, and no practicable imaging algorithm. In the dissertation, the theory and algorithms of CS based radar imaging is discussed and applied to high-resolution radar imaging. The major works include the following three parts: the CS based radar imaging data acquisition methods, the CS based radar imaging algorithms and the application of CS in wide-band radar imaging. Firstly, we establish the sparse representation models of the baseband echo under both matched filtering and de-chirp processing, and propose digital or analog realization scheme of analog-to-information convertor in radar receiver. Secondly, we realize a phase-reservation CS based range compression algorithm, constructe a CS based radar imaging framework with range and azimuth decoupled and apply it to both 2D and 3D radar imaging combined with conventional imaging algorithms. Finally, we apply the CS based imaging method to wideband radar imaging system. The effectiveness of the proposed algorithms are tested through processing both simulation and real data.
The major contributions of the dissertation are summarized as follows:
In the study of CS based radar data acquisition methods, we firstly analyze the radar echo signal, and then establishe the sparse representation models of the processed signals under matching filter mode and de-chirp mode. Aiming to real-time measurement, we introduce Analog-to-Information converter(AIC) into compressive sensing imaging processing, and proposes both digital and analog solutions of AIC in radar receiver.
In the study of CS based imaging algorithms, we firstly select the sparse signal reconstruction algorithm suitable for radar imaging, and then propose a phase-reserve CS range compression algorithm combined with sparse representation of radar echo signal and non-correlation measurement matrix. Finally, we propose a range-azimuth decoupling radar imaging frame, in which CS range compression algorithm is combined with traditional radar 2D imaging and 3D imaging algorithms so as to realize the CS imaging algorithm.
In the study of CS based wide-band radar imaging algorithm, we propose a CS measurement method for stepped-frequency chirp signal (SFCS) and realize CS imaging for wide-band radar with application of the subaperture processing method of SFCS.
引文
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