Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

Spotlight SAR sparse sampling and imaging method based on compressive sensing

Spotlight synthetic aperture radar(SAR)emits a chirp signal and the echo bandwidth can be reduced through dechirp processing,where the A/D sampling rate decreases accordingly at the receiver.Compressive sensing allows the compressible signal to be reconstructed with a high probability using only a few samples by solving a linear program problem.This paper presents a novel signal sampling and imaging method for application to spotlight SAR based on compressive sensing.The signal is randomly sampled after dechirp processing to form a low-dimensional sample set,and the dechirp basis is imported to reconstruct the dechirp signal.Matching pursuit(MP)is used as a reconstruction algorithm.The reconstructed signal uses polar format algorithm(PFA)for imaging.Although our novel mechanism increases the system complexity to an extent,the data storage requirements can be compressed considerably.Several simulations verify the feasibility and accuracy of spotlight SAR signal processing via compressive sensing,and the method still obtains acceptable imaging results with 10%of the original echo data.     

Suppressing azimuth ambiguity in spaceborne SAR images based on compressed sensing

In spaceborne synthetic aperture radar,undersampling at the rate of the pulse repetition frequency causes azimuth ambiguity,which induces ghost into the images.This paper introduces compressed sensing for azimuth ambiguity suppression and presents two novel methods from the perspectives of system design and image formation,known as azimuth random sampling and ambiguity separation,respectively.The first method makes the imaging results for the ambiguity zones as disperse as possible while ensuring that the imaging results for the main scene are affected as little as possible.The second method separates the ambiguity signals from the echoes and achieves imaging results without the ambiguity effect.Simulation results show that the two methods can reduce the ambiguity levels by about 16 dB and 99.37%,respectively.     

Sparse microwave imaging:Principles and applications

This paper provides principles and applications of the sparse microwave imaging theory and technology.Synthetic aperture radar(SAR) is an important method of modern remote sensing.During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology,and at the same time encountered increasing complexity in system implementation.The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory,new system and new methodology of microwave imaging.Based on classical SAR imaging model and fundamental theories of sparse signal processing,we can derive the model of sparse microwave imaging,which is a sparse measurement and recovery problem and can be solved with various algorithms.There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging,including sparse representation,measurement matrix construction,unambiguity reconstruction and performance evaluation.Based on these considerations,the sparse signal processing could be successfully applied to radar imaging,and achieve benefits in several aspects,including improvement of image quality,reduction of data amount for sparse scene and enhancement of system performance.The sparse signal processing has also been applied in several specific radar imaging applications.     

星载SAR目标回波信号模拟源的研制

结合我国星载SAR系统的研制,中国科学院电子所成功研制出地面测试用的高精度星载合成孔径雷达目标回波信号模拟源(SSARES)。根据星载SAR回波的信号模型,模拟生成雷达回波信号对SAR系统实行半实物仿真测试,得到了准确可靠的试验结果。SSARES为星载SAR系统的研制提供了一种行之有效的测试手段。     

基于SAR回波仿真的BAQ压缩性能研究

分析了合成孔径雷达（Synthetic Aperture Radar,SAR）回波的统计特性,引入了基于SAR分布目标回波仿真的方法,研究了分块自适应量化（Block Adoptive Quantization,BAQ）压缩对SAR回波信号特性的影响。然后介绍了BAQ算法原理,以SAR回波的量化噪声因子、相关系数和SAR图像的辐射分辨率为评估指标。利用郊区和城市两幅机载SAR图像进行试验,分析了不同压缩比对两块地形目标的影响效果,试验表明,在保证成像质量的前提下,郊区地形可以采用比城市地区更高的压缩比。     

匀速运动目标的聚束SAR成像特征分析

运动目标的合成孔径雷达(SAR)成像特征是SAR/GMTI系统中进行运动目标检测和成像的基础.以往研究都是在两维可分离条带SAR成像算法条件下讨论目标运动对成像的影响,而很少考虑高分辨聚束SAR成像算法处理后运动目标的成像特征.本文推导了两种典型高分辨聚束SAR成像算法(RMA和PFA)处理后的动目标误差谱表达式,并在此基础上从目标几何定位误差、残留距离徙动和散焦等方面给出了完整的聚束SAR运动目标响应特征分析.最后通过仿真数据处理验证了分析结果.     

近程合成孔径雷达子孔径数据的成像处理

研究了近程SAR的子孔径数据成像问题。通过时SAR子孔径数据进行的聚束处理,在短时间完成对波束覆盖的目标区域成像并形成“快视”,从而降低成像时SAR载体运动补偿的要求。为解决近距离大前斜视角成像问题并避免复杂的插值计算,成像算法采用了基于波前重构理论的精确的Range Stacking算法。仿真和实际飞行试验数据的成像处理结果证明了本文方法的有效性。     

合成孔径雷达及其干涉技术研究进展

合成孔径雷达(Synthetic aperturer adar,SAR)能够在全天候、全天时条件下对地面进行大范围测绘,是现代民用遥感和军事侦察中的重要手段。本文回顾了SAR及干涉合成孔径雷达(InSAR)技术的历史,叙述了SAR由非聚焦到完全聚焦,由光学处理到全数字式处理,由二维测绘到干涉三维测绘的发展历程。通过例举典型系统,介绍了国外机载、空载SAR和InSAR技术的现状,并对我国近年来在该领域取得的进展作了简要介绍。最后,本文给出对SIR—C／X—SAR采集的航天飞机SAR数据处理所得到的成像结果。     

基于稀疏线性调频步进信号的ISAR成像

线性调频步进信号在简化雷达系统设计的同时,也存在对高速运动目标易出现Doppler模糊的问题,因此研究如何提高其等效的重复频率具有重要意义.由于ISAR目标的后向散射场具有较强的稀疏性,即大部分能量仅由少数散射中心贡献,所以本文基于稀疏信号表示理论,通过对目标回波模型的分析,提出了一种基于稀疏步进频率信号的逆合成孔径雷达成像方法.该方法通过随机地选择线性调频步进信号的部分子脉冲进行发射,然后使用稀疏信号分解的方法对目标图像进行重建以得到目标的二维高分辨图像.该方法以计算资源为代价,能够有效地去除方位Doppler模糊,同时还能够压低旁瓣并得到超分辨的图像.仿真和实测数据处理结果验证了本文方法的有效性.     

ISAR imaging via sparse frequency-stepped chirp signal

Frequency-stepped chirp signal can simplify the designation of radar system.However,it has a shortcoming of Doppler ambiguity for high-speed moving targets.Therefore,it is of great significance to study how to increase its equivalent pulse repeat frequency.The back scattering field of the ISAR target has strong sparsity;that is to say,most energy is contributed merely by a few scattering centers.Hence,based on the theory of the sparse signal representation,a novel method for ISAR imaging via sparse frequency-stepped chirp signals is proposed by analyzing the signal model of the target.In the proposed method,part of sub-pulses of the frequency-stepped chirp signal is randomly selected to transmit,and then the 2D high-resolution image of the target can be constructed by sparse signal decomposition.At the cost of computational resources,the method can effectively resolve the problem of Doppler ambiguity,decrease the sidelobes and obtain a super-resolution image.Furthermore,the validity of the proposed approach is confirmed by the results of numerical simulations and real data.     

Two modified discrete chirp Fourier transform schemes

This paper presents two modified discrete chirp Fourier transform (MDCFT) schemes. Some matched filter properties such as the optimal selection of the transform length, and its relation-ship to analog chirp-Fourier transform are studied. Compared to the DCFT proposed previously, theo-retical and simulation results have shown that the two MDCFTs can further improve the chirp rate reso-lution of the detected signals.     

Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing(CS)is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements.Based on this idea,this paper proposes a novel synthetic aperture radar(SAR)imaging approach by exploiting sparseness of echo data in the fractional Fourier domain.The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices.Experimental results have shown that,the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique.Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.     

合成孔径高度计的距离校正

在高度计中使用合成孔径技术正在成为当前国际上高度计技术领域内一个新的研究热点。本文首先简单介绍了传统雷达高度计的测距原理,然后介绍了在雷达高度计中引入合成孔径技术的思想。通过分析高度计中合成孔径处理的特点,说明在高度计中使用合成孔径技术时,应该进行距离校正。本文从高度计的多普勒波束锐化对应的几何模型和合成孔径雷达回波信号的相位分析两个角度分析了距离校正的方法,给出了距离校正的思路和校正公式。