空间轨道目标ISAR成像方法

研究了高速空间目标逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)成像问题;根据空间目标回波模型提出了先进行速度补偿再进行平动补偿的ISAR成像方法。研究了高速运动目标回波模型,针对空间目标回波为线性调频信号的特点,提出采用CLEAN算法的线性调频信号参数估计方法对回波进行速度补偿。最后对自旋和非自旋两类轨道飞行目标成像进行了分析。仿真结果验证了理论分析的正确性和成像方法的有效性。     

频率步进雷达多目标ISAR成像方法

针对频率步进雷达同一波束内多个运动目标在径向上重叠 而无法分辨时的ISAR成像问题,提出了一种基于调频傅里叶变换的ISAR多目标成像新方法。 在构建频率步进雷达多目标回波信号模型的基础上,采用调频傅里叶变换精确估计各个目标 的速度参数,结合 Clean方法实现对多目标回波信号的分离,完成多目标ISAR成像。仿真实 验结果进一步验证了文章所采用方法的有效性。     

基于线性正则变换的复杂运动目标的3-D InISAR成像

二维的逆合成孔径雷达(two-dimensional inverse synthetic aperture radar, 2-D ISAR)成像是目标三维(three-dimensional, 3-D)几何结构在成像平面的投影,逐渐难以满足现代工程中目标识别与分类的需求,特别是复杂运动目标.近年来,干涉ISAR (interferometric ISAR, InISAR)成像技术能够高分辨地重构3-D目标的几何结构而颇受关注.本文提出一种基于线性正则变换(linear canonical transform, LCT)的复杂运动目标的3-D InISAR成像算法.首先,结合目标复杂运动分析,给出了目标所产生的原始回波信号.经过运动补偿和图像对准后,将方位向回波建模为线性调频(linear frequency modulation, LFM)信号.其次,根据LFM信号的时频特性,本文提出了基于LCT的2-D ISAR成像算法.该算法不仅能够获取聚焦的多通道2-D ISAR图像,而且很好地保留了相位信息.最后,利用干涉技术,提出了新的3-D InISAR成像算法,实现了目标的高分辨3-D几...     

轨道目标ISAR中频回波模拟技术研究

该文目的是研究一种空间目标宽带ISAR雷达回波的模拟实现方法，为空间目标探测雷达的研制及空间目标特性的研究提供信号来源。该文首先深入研究了空间目标的运动特性，给出了弹道目标轨道方程的计算方法，由目标的轨迹可以确定任意时刻目标的姿态，从而得到目标的散射中心分布。然后分析了目标在大时宽带宽雷达照射下的回波信号特性，结合目标轨道计算提供的散射中心分布信息，模拟出目标的中频回波信号。最后，提出了一种轨道目标ISAR雷达回波模拟器的实现方案，给出了实验结果。在实际应用中证明，用该方法模拟空间目标宽带雷达回波完全可行。     

基于图像旋转匹配的组网雷达ISAR图像横向定标

基于组网雷达观测模型,分析了在多部雷达同时观测条件下所得ISAR图像之间的对应关系,提出了一种基于图像旋转匹配的转角估计办法,通过对旋转速度的一维搜索实现了目标角速度的估计。仿真结果表明,本算法能基于组网雷达所获得的多幅ISAR图像实现目标转角的稳健估计,实现ISAR图像横向定标。所提算法可对慢运动目标或平动补偿后的回波信号进行处理,适用于不同带宽、不同中心频率的宽带雷达组网形式。     

高频区复杂目标宽带雷达特征信号仿真

高频区复杂目标宽带雷达特征信号包括日标的高分辨一维距离像和两维SAR／ISAR图像,高频区复杂目标宽带雷达特征信号仿真对雷达目标识别和SAR／ISAR图像解译具有重要意义。该文在现有高频区复杂目标RCS特征信号计算方法的基础上,重点研究了高频区复杂目标宽带雷达特征信号包括目标的高分辨一维距离像和两维SAR／ISAR图像的仿真方法。并通过仿真目标和实际复杂目标的高频区宽带雷达特征信号的仿真实验结果验证了该文方法的有效性。     

基于最大变差范数准则的ISAR自聚焦方法

为消除目标平动引起的初相误差,必须进行自聚焦以避免ISAR图像模糊.在分析ISAR回波信号模型的基础上,本文构造了高阶多项式相位信号的初相补偿函数.已有文献多ISAR图像聚焦程度为准则,对该多项式相位信号的参数进行优化.本文利用最大全变差范数作为ISAR方位向成像的聚焦评价准则,该指标值在平动参数空间中的分布具有局部极值点少的优点,利于最优确定初相补偿函数的参数,并采用协同粒子群优化算法加速参数的寻优速度和精度.仿真实验证明了本文方法的可行性和正确性.     

基于雷达散射特性的复杂目标SAR图像仿真

高频区复杂目标宽带雷达特征信号仿真,尤其是两维SAR图像的仿真对雷达目标识别和SAR图像解译具有重要意义.根据复杂目标三维模型运用物理光学(PO)法与增量长度绕射系数(ILDC)法结合的改进图形电磁算法快速计算目标雷达散射截面,而后通过对不同SAR成像模式的研究建立SAR仿真成像系统,从而分别仿真得到StripSAR、SpotSAR、ISAR图像.仿真结果与真实图像相比具有较好的相似度,验证了方法的有效性.     

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.     

具有大旋翼一类旋转部件的雷达目标成像

由于目标本身或目标部件的振动和旋转运动而引入的微多普勒谱会对目标主体的ISAR像造成污染,严重时无法对目标主体进行成像。介绍了雷达目标回波信号中的微多普勒信号产生原理,提出了一种微多普勒信息分离与提取的方法,基于旋转部件的微多普勒谱与目标主体谱在谱图域表现形式的不同,将旋转部件的微多普勒谱与目标主体谱分离后获得了具有大旋转部件一类目标的清晰像,同时也获得了目标旋转部件的一些运动及结构信息。最后,仿真结果验证了此方法的有效性。     

Comparison of parametric sparse recovery methods for ISAR image formation

A parametric sparse representation model of the inverse synthetic aperture radar(ISAR)signal has been proposed recently,and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate.Based on the parametric sparse representation model,several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image.In this paper,four parametric sparse recovery algorithms are compared mainly in three aspects:the accuracy of the rotation rate estimation,the ISAR image quality and the computational load.Numerical examples are presented to show the advantages and disadvantages for each method.     

利用序列ISAR图像获取空间目标3-D信息的方法

如何从空间目标序列性二维(2-D,Two-Dimentional)逆合成孔径雷达(ISAR,Inverse Synthetic Aperture Radar)成像获取目标的三维(3-D)信息,是目标特征自动识别(ATR,Automatic Target Recognition)技术的重要研究课题。利用双向射线跟踪(BART,Bidirectional Analytic Ray Tracing)方法,计算连续多角度观测条件下空间目标的电磁散射数据,并由此获取空间目标的ISAR序列2-D图像。再利用KLT(Kanade-Lucas-Tomasi)特征跟踪算法,跟踪提取2\|D序列ISAR图像中的特征点(强散射点),获得其2-D坐标。然后,基于正交因式分解法(OFM,Orthographic Factorization Method),计算强散射点的3\|D坐标,获取空间目标的3-D信息。通过简单六棱柱模型,验证重构算法的精度;并以ENVISAT卫星模型为例,给出强散射点的3-D重构结果。结果表明,本文对空间目标3\|D信息获取方法能有效地从ISAR序列2-D图像中重构目标的三维信息。     

Target detection of ISAR data by principal component transform on co-occurrence matrix

Issue of automated target detection in ISAR can be stated as what features enhance objects of interest from the rest of the data. Much experimentation done in this area have used Fourier transforms for preprocessing the raw signal data. Generally the ISAR data are comes with a matrix of complex number values and therefore intuitive logic appears to favor a Fourier transform. A hypothesis was made that a Fourier transform in preprocessing may mask some data that could be part of feature used to threshold the object from background. Thus a trial was done on MATLAB simulated ISAR data to see if such data can be transformed into a matrix to visualize objects by preprocessing with principle component transform followed by some modification conventional thresholding techniques i.e. gray level co-occurrence matrix. Since it would be difficult to do so in complex valued matrices, these matrices had been decomposed to real valued and the imaginary valued matrices separately. Advantages of simulated data were that variables could be defined and changes in preprocessing transform and thresholding result could be compared with significant accuracy before a trial with actual performance of ISAR imagery. The preliminary result in this paper does show that preprocessing transform need not be Fourier. Principle component transform may bring about features that enhance thresholding values for Automatic target detection. Thresholding in conventional methods is done by finding a fixed value to create a binary image highlighting the object. In the modification proposed here single value thresholding objects and then spatially locating the object in a binary matrix may circumvented.     

基于超声波传感器多目标定位系统

设计和制作了一种用于导盲系统的定位多个障碍物位置的超声波传感器阵列装置,它只需要一个探测周期,就可以实现对单目标和多目标的准确定位,比传统的多超声系统具有更高的探测效率.在超声波测距系统中,精确地确定电路系统的延时参数是提高测距精度的关键,本文应用最小二乘的数学方法可以精确地获得超声波信号在电路中的传播时间,有效地提高系统测距精度,在导盲定位系统中得到了成功的应用.利用几何学的定位方法融合被修正的距离数据,实现了对目标点的准确定位.通过数据关联来区分多个目标.     

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.     

基于图像熵的线性阵列ISAR成像处理方法

研究一种线性阵列逆合成孔径雷达(LAAISAR)的成像处理方法。分析线性阵列ISAR的几何模型并给出成像处理流程。针对阵列ISAR同时接收多路回波信号的特点,采用传统的距离-多普勒算法对每路回波进行成像;利用互相关法进行图像粗配准;在此基础上利用图像熵法进行图像细配准,得到匹配的目标图像。基于该处理流程的仿真实验表明,该处理方法能够充分利用阵列ISAR的每路回波信息,使目标图像精确配准,且能有效提高目标分辨率。     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.