Coherent processing for ISAR imaging with sparse apertures

To implement target detection,tracking and imaging in a multifunctional radar system,the wideband measurements for inverse synthetic aperture radar(ISAR)imaging are usually sparsely recorded.Considering the incoherence problem in such sparse-aperture ISAR(SA-ISAR)systems,we concentrate on the study of a coherent processing method in this work.Based on an all-pole model,the incoherence parameters between abutting sub-apertures can be effectively estimated.After coherence compensation,an optimization-based SAISAR imaging approach is provided from the view of statistics.Simulation and real data experiments validate the feasibility and effectiveness of the proposals.     

Use of 3D ship scatterer models from ISAR image sequences for target recognition

Traditionally, inverse synthetic aperture radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of time-averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, however, also provides additional information about the target, which can be used to construct a 3D representation of the target scatterer positions. In this paper, a method for classifying a ship based on 3D scatterer information from a sequence of 2D ISAR images is described. A preliminary classification result for simulated ISAR images of nine types of ship is also provided.     

ISAR imaging of maneuvering target with complex motions based on ACCF–LVD

This paper considers the inverse synthetic aperture radar (ISAR) imaging problem for a maneuvering target with complex motions, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent integration period of radar imaging, which will degrade the imaging quality. A nonsearching ISAR imaging algorithm based on adjacent cross correlation function (ACCF) and Lv's distribution (LVD), i.e., ACCF–LVD, is proposed, where the ACCF is applied to correct the RM and reduce the order of DFM. Then the motion parameters are estimated via LVD and Fourier transform. With the estimated motion parameters, high quality ISAR images can be achieved. The advantage of the presented method is that it can estimate the motion parameters under low signal-to-noise ratio (SNR) without searching procedures. Finally, several simulation examples are shown to confirm the validity of the proposed algorithm.     

导弹目标逆合成孔径雷达成像的仿真

该文应用物理光学法仿真出平面波以任意角度、不同频率入射时某型导弹的电磁散射回波矩阵,并利用快速傅立叶变换成像处理算法得出目标的像。导弹目标利用部件分解法进行了近似,并引进了散射矩阵的概念而简化了导弹目标回波相关数值计算复杂度,从而提高逆合成孔径雷达成像(ISAR)的实时性。文中运用信号处理相关理论对平面波在不同的入射角下逆合成孔径雷达对导弹目标所成的像进行了分析,所成像能精确地反映出目标地镜像散射区域及目标的旋转情况。此仿真结果表明物理光学法能够简单有效地计算出导弹目标的散射回波并对其进行逆合成孔径雷达成像。     

基于ISAR成像的目标大角度RCS外推

传统逆合成孔径雷达(ISAR)转台成像算法对目标进行成像后只能外推小角度区域雷达散射截面(RCS)。针对这一问题,建立了大转角转台成像系统,提出了一种基于近场微波成像的RCS外推算法,分析了近场大转角转动带来的越距离单元徙动问题。通过将距离与方位域解耦,并对方位方向进行圆周卷积运算,得到高质量的目标成像,进而可以外推目标360度方位的RCS,并与传统ISAR小角度成像算法进行比对。仿真结果表明,外推算法不仅可以对目标进行成像,诊断目标的强散射点位置,还可以用来做远区RCS大角度外推,并结合成像结果分析目标散射机理。     

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.     

绕环天线的干涉ISAR测高算法

提出了两副雷达天线在平面内绕环时的干涉逆合成孔径雷达(Inverse synthetic aperture radar, ISAR)成像算法.现有的干涉ISAR成像研究通常忽略了天线几何位置对成像影响,将天线设置为仅在高度上有差别的两副垂直天线.针对这一情况,本文推导了当两副雷达天线不垂直时、特别是在平面内圆周绕环时的干涉ISAR测高算法.研究结果表明,干涉ISAR高程测量不仅与基线长度有关,还与散射点横距坐标有关,于是需要对散射点横距进行估计.仿真结果验证了在此情况下干涉ISAR测高公式的正确性,并进一步分析了天线基线与有效基线之间夹角的变化对测高精度的影响.经统计,目标高度平均测量误差低于3%,几乎达到原两副天线垂直时的测高精度.     

小转角下空间目标双基地ISAR二维成像算法

为了解决小转角下空间目标双基地ISAR方位向分辨率下降的问题,提出基于AR模型数据外推的双基地ISAR成像算法.双基地ISAR成像时,小转角下的方位向回波可认为服从AR模型,据此建立线性预测方程,利用Burg熵最大法中的Levions递推估计预测系数,然后对方位向回波进行外推,最后基于原始数据和外推后的数据共同进行谱估...     

Ship identification in sequential ISAR imagery

We have developed an online system that automatically identifies ships observed in a rapidly updating sequence of range-Doppler images produced by inverse synthetic aperture radar (ISAR). In the system, in order to cope with the invariable noise due to the physics of imaging, we propose to employ a multiframe image processing algorithm that stably extracts profiling as a basic feature reflecting all characteristics of a target. For ship identification, representing the extracted profiles as high-dimensional vectors, we adapt the vector analysis using the recently proposed constrained mutual subspace method (CMSM). The system currently works on an ordinary PC at 5 frames/s and achieves feasible performance of identification. The system is verified using simulated data.Received: 22 September 2002, Accepted: 22 March 2004, Published online: 27 May 2004 Correspondence to: Atsuto Maki. Currently with the Graduate School of Informatics, Kyoto University     

步进线性调频信号舰船ISAR成像的实时信号处理

针对舰船目标的复杂运动特性以及采用步进线性调频信号给实时处理带来的困难,本文设计了以数字信号处理器作为处理单元数字信号处理板卡,选择了适于步进线性调频信号舰船逆合成孔径雷达(ISAR)成像实时处理的算法流程,并完成了实时处理在信号处理板卡上的任务分配和程序编写.该方法能够完成步进线性调频信号舰船目标ISAR成像的实时处理任务,外场试验的结果验证了本文方法的有效性.     

ISAR imaging of complex motion targets based on Radon transform cubic chirplet decomposition

For targets with complex motion, the echo of inverse synthetic aperture radar (ISAR) is a time-varying frequency signal in azimuth. Hence, the traditional range-Doppler (R-D) algorithm based on a constant frequency is invalid. In this letter, a novel ISAR imaging method for targets with complex motion is presented. The echo in azimuth is characterized as an amplitude-modulated (AM)-cubic phase signal, which is closer to the real ISAR scene, and Radon transform cubic chirplet decomposition (RTCCD) algorithm is proposed to process the signal. By introducing Radon transform and improved cubic chirplet function (CPF), the proposed algorithm estimates the chirp rate and the cubic chirp rate simultaneously to avoid error accumulation. Therefore, the parameter estimation precision is improved, and a high quality ISAR image can be obtained. Simulations and real data experiment validate the effectiveness of the proposed method.     

Sparse SAR imaging based on L1/2 regularization

In this paper,a novel method for synthetic aperture radar(SAR)imaging is proposed.The approach is based on L1/2 regularization to reconstruct the scattering field,which optimizes a quadratic error term of the SAR observation process subject to the interested scene sparsity.Compared to the conventional SAR imaging technique,the new method implements SAR imaging effectively at much lower sampling rate than the Nyquist rate,and produces high-quality images with reduced sidelobes and increased resolution.Also,over the prevalent greedy pursuit and L1 regularization based SAR imaging methods,there are remarkable performance improvements of the new method.On one hand,the new method significantly reduces the number of measurements needed for reconstruction,as supported by a phase transition diagram study.On the other hand,the new method is more robust to the observation noise.These fundamental properties of the new method are supported and demonstrated both by simulations and real SAR data experiments.     

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LBI的二维复稀疏信号重建算法及应用研究

针对二维复稀疏信号重建时存在存储空间和计算复杂度增加的问题, 本文提出了一种快速并行重建二维复稀疏信号的并行线性Bregman迭代(Parallel fast linearized Bregman iteration, PFLBI)算法. 首先, 构建了二维复稀疏信号的结构模型以及PFLBI算法基本迭代格式; 其次, 通过变步长方式提高迭代收敛速度, 而每次迭代的步长则是通过估计中间变量的积累量突破收缩阈值需要的积累步数得到的; 再次, 对算法的性能指标进行了分析; 最后, 将该算法应用于逆合成孔径雷达(Inverse synthetic aperture radar, ISAR)成像. 实验结果表明该算法在重建性能和速度上具有优势.     

Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering

Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.     

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.     

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。     

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization基于伪极坐标变换和原子范数最小化的网格偏离目标DLSLA 3-D SAR成像方法

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.     

Structured Bayesian compressive sensing with spatial location dependence via variational Bayesian inference

In this paper, a novel non-parametric Bayesian compressive sensing algorithm is proposed to enhance reconstruction performance of sparse entries with a continuous structure by exploiting the location dependence of entries. An approach is proposed which involves the logistic model and location-dependent Gaussian kernel. The variational Bayesian inference scheme is used to perform the posterior distributions and acquire an approximately analytical solution. Compared to the conventional clustered based methods, which only exploit the information of neighboring pixels, the proposed approach takes the relationship between the pixels of the entire image into account to enable the utilization of the underlying sparse signal structure. It significantly reduces the required number of observations for sparse reconstruction. Both real-valued signal applications, including one-dimension signal and two-dimension image, and complex-valued signal applications, including single-snapshot direction-of-arrival (DOA) estimation of distributed sources and inverse synthetic aperture radar (ISAR) imaging with a limited number of pluses, demonstrate the superiority of the proposed algorithm.