Clutter suppression and moving target imaging approach for multichannel hypersonic vehicle borne radar

In this paper, a clutter suppression and ground moving target imaging algorithm is proposed for the hypersonic vehicle (HSV) borne multichannel synthetic aperture radar (SAR) system. Different from the traditional horizontal flight radar platforms, the HSV-borne radar platform is with a complex movement, and typically has a skipping trajectory. Therefore, the clutter suppression and imaging operations are difficult to implement. To deal with this problem, a descending stage signal model of multichannel SAR system is presented and studied for the HSV-borne radar. Then, the clutter and moving target echoes are transformed into the azimuth frequency domain. Considering that there exists a phase difference between clutter and the moving target, the clutter suppression approach is implemented with a series of spatial domain filters. After that, the moving target is focused based on the keystone transform. In addition, the performance analyses and related issues of the proposed method are presented. Finally, some simulation experiments are taken to validate the effectiveness of the proposed method.     

Ground moving target detection and location based on SAR images for distributed spaceborne SAR

In recent years, a new concept of spaceborne SAR implementation has been propos- ed[1,2], i.e., the distributed (constellation) spaceborne SAR implementation in which sev-eral formation-flying small satellites (or micro-satellites) cooperates to perform multiple missions. The coherent combination of ground echoes acquired by all small satellites can significantly improve the performance of ground moving target indication (GMTI), SAR mapping, interferometric SAR (InSAR), etc. Since the …     

MIMO SAR OFDM chirp waveform design and GMTI with RPCA based method

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.     

一种鲁棒的三通道SAR慢动目标检测、定位算法研究

该文研究三通道合成孔径雷达(SAR)在通道不平衡条件下的慢动目标检测、定位问题。实际系统中,由于雷达系统存在通道幅相不一致等误差,干涉SAR/GMTI技术的杂波抑制性能急剧下降,无法检测慢动目标并进行定位。本文提出了一种分块SAR图像通道误差校正算法,进而采用空域自适应处理对慢动目标进行检测及定位。仿真实验表明该方案对系统误差的敏感度显著下降,具有很好的鲁棒性,非常适合实际工程实施。     

An approach for refocusing of ground fast-moving target and high-order motion parameter estimation using Radon-high-order time-chirp rate transform

Long synthetic aperture time can improve the imaging quality of a ground moving target, whereas a moving target may be severely smeared in the cross-range image due to the range migration and the Doppler frequency migration. In this paper, the effects of the third-order Doppler broadening and Doppler ambiguity of a fast-moving target are considered. To address these issues, a novel motion parameter estimation method named high-order time-chirp rate transform (HTRT) is proposed, and then a new synthetic aperture radar (SAR) imaging method based on Radon-HTRT (RHTRT) for a ground moving target is developed. The major contributions are as follows: 1) The proposed SAR imaging method can eliminate the Doppler ambiguity effect. 2) The proposed method can realize longer time coherent integration than Radon–Fourier transform (RFT) and Radon–fractional Fourier transform (RFRFT) methods. 3) The proposed method is computationally efficient since HTRT can obtain the motion parameters of a moving target via performing the 2-dimensional (2-D) fast Fourier transform (FFT). Both the simulated and real data processing results show that the proposed method can finely image a ground moving target in a high signal-to-clutter and noise ratio (SCNR) environment.     

基于小波域HMT的图像杂波抑制方法

针对复杂背景下红外微弱点状运动目标的检测,提出一种基于小波域HMT模型的图像杂波抑制方法。对图像小波系数低频部分建立隐马尔可夫树模型,使用Bayesian准则估计图像背景小波系数,参照杂波抑制模型,得到杂波抑制后图像的信号加噪声模型,并通过计算Kendall秩相关系数和Friedman统计量验证了该方法残留噪声的高斯性和独立性。     

机载雷达级联降维空时自适应杂波抑制方法

提出一种机载雷达杂波抑制的级联降维空时自适应算法,即,先对全空时两维接收数据进行预滤波处理,将杂波局域化,降低杂波自由度;然后对预处理输出的信号的相关矩阵进行子阵划分,求解低维权向量,进一步降低运算量和采样要求。理论分析和实验仿真结果表明,所提算法具有良好的收敛性能和杂波抑制能力,并且对于阵元随机幅相误差和杂波起伏具有很好的容差能力。基于实测数据的实验验证了算法的有效性和稳健性。     

多通道SAR杂波抑制技术研究

主要研究了几种典型的多通道SAR杂波抑制技术:相位中心偏置(DPCA)技术、沿航迹干涉(ATI)技术和空时自适应处理(STAP)技术,探讨了对机载单天线SAR实际数据应用复图像域DPCA、ATI杂波抑制技术和空间频率域STAP杂波抑制技术实现运动目标检测,并给出一些实际数据的实验结果。     

分布式星载SAR-GMTI系统仿真

仿真是进行分布式星载SAR地面动目标检测(GMTI)研究的重要方式。对分布式星载SAR-GMTI的系统仿真进行了全面讨论,内容包括地杂波建模\,动目标建模\,回波仿真方法、信号处理流程和方法,搭建了一个完整的仿真系统,用实验结果验证了该系统的可靠性。     

基于自聚焦的变门限SAR/GMTI动目标检测方法

高分辨率合成孔径雷达（Synthetic aperture radar,SAR）合成孔径时间长导致动目标散焦严重、信杂噪比（Signal clutter noise ratio,SCNR）降低,增加了对动目标的检测难度。针对这种情况,本文提出了一种基于自聚焦的变门限SAR/(Ground moving target indication,GMTI)动目标检测算法。该算法利用距离相位误差和方位相位误差之间存在的固定关系实现对动目标的两维相位误差的估计与补偿,达到提高SCNR的目的。通过两级不同门限的恒虚警率（Constant false alarm rate,CFAR）检测分别实现动目标检测和排除虚警。实测数据结果表明,本文算法能有效地检测动目标,降低虚警概率。     

利用高斯混合模型的SAR图像目标CFAR检测新方法

SAR（合成孔径雷达）图像杂波分布模型种类繁多且对实际地物的建模能力有限。在使用基于杂波统计模型的CFAR(恒虚警率)算法对SAR图像进行目标检测时,杂波统计模型的失配会导致检测结果产生较大的CFAR损失,算法精度不高。提出了一种基于高斯混合模型的CFAR检测新方法。该方法以理论上可以拟合任意形状概率密度分布的高斯混合模型对实际SAR图像的背景杂波进行拟合,利用拟合后得到的分布模型,根据CFAR检测的原理推导出目标检测阈值的计算公式完成目标的检测。新方法对服从不同分布模型的背景杂波,使用形式上统一的模型进行描述,克服了CFAR检测高度依赖背景杂波分布的缺点,提高了CFAR的通用性。实验结果表明,即使在背景杂波类型未知的情况下,新方法依然得到了良好的目标检测效果。     

基于矩阵分解的海洋SAR图像舰船检测

针对海洋原始图像与低秩和稀疏矩阵分解模型数据结构不一致的问题,本文提出一种新的基于矩阵分解的海洋SAR图像舰船检测方法。首先该方法需对结构化相似的海洋SAR图像进行重组;然后根据重组矩阵特性适应性设计一个分解精度更高、分解速度更快的新矩阵分解模型,并利用增广拉格朗日乘子法求解模型,在不依赖任何杂波模型和检测统计量的前提下,实现代表舰船目标的稀疏成分的提取;最后利用形态学处理进行优化,实现海洋SAR图像舰船目标的检测。基于高分三号SAR卫星数据的实验结果表明,相比已有的基于鲁棒主成分分析的舰船检测方法,本文方法在处理复杂海况时,能更快速度地以较好的形状从海杂波中准确提取舰船目标,具有更好的鲁棒性。     

Proposal-Copula-Based Fusion of Spaceborne and Airborne SAR Images for Ship Target Detection⁎⁎

In this paper, we consider the problem of fusion of synthetic aperture radar (SAR) images from spaceborne and airborne sensors and investigate its applications to inshore ship target detection. Existing SAR image fusion methods mainly focus on image denoising or texture enhancement, but show limited improvement of target-to-clutter ratios (TCRs) in composite images and lead to deteriorated target detection performance. To address this issue, we propose a new method for the fusion of spaceborne and airborne SAR images based on the target proposal and the copula theory (TPCT). In TPCT, target and clutter correspondence between different images are exploited to improve the TCRs of composite images. TPCT consists of three steps. First, target proposals are extracted from spaceborne and airborne SAR images and then fused to enhance the common ship target areas therein. Second, a new method to construct the joint probability density function (PDF) of clutter in spaceborne and airborne SAR images is presented to model the statistical dependence of clutter therein based on the copula theory. This copula-based joint PDF is used to suppress the clutter areas remained in the intersection of target proposals. Third, clues from the intersection of target proposals and the copula-based joint PDF of clutter are fused by the Hadamard product to generate the composite image with enhanced ship targets and the suppressed clutter. Experimental results based on measured spaceborne and airborne SAR data show that the proposed TPCT fusion method leads to higher TCRs of composite images and better performance in the ship detection task than other commonly used image fusion methods.     

一种机载斜视阵杂波谱补偿的降维STAP方法

在机载斜视阵雷达中,杂波谱对距离有依赖性导致了传统空时二维处理难以抑制杂波.根据杂波的分布特性,提出了一种杂波谱补偿的降维方法.该方法采用导数更新法级联多级维纳滤波器.首先通过导数更新方法对杂波谱进行有效的补偿,消除了杂波谱对距离的依赖性,然后使用多级维纳滤波器对数据进行分解降维处理,避免了矩阵求逆的运算,降低了数据的运算量.理论分析和仿真结果表明,本文方法既可以有效消除杂波谱对距离的依赖性,准确地估计出互相关系数,同时减少了运算量.     

综合边界和纹理信息的合成孔径雷达图像目标分割

目的 针对传统Grab Cut算法需要人工交互操作,无法实现合成孔径雷达（SAR）图像的自动分割,且方式单一（仅利用边界或纹理信息中的一种）的问题,提出一种综合利用边界和纹理信息的改进Grab Cut算法,实现对SAR图像目标的自动分割。方法 首先将其他格式的彩色或灰度SAR图像转化为24 bit的位图,采用图形理论对整幅SAR图像建模,根据最大流算法找到描述图的能量函数最小的割集,从而分割出目标区域;然后采用中值滤波抑制相干噪声;最后通过邻域生长算法滤除图像斑点和小目标的干扰,从而达到目标边界的连接,实现自动对SAR图像中的目标进行分割。结果 在64位Window 7环境下采用MATLAB R2014处理平台,对楼房、车库、大树、汽车群等4幅分辨率不同的SAR图像进行目标分割实验,特征目标被自动分割出来,耗时分别为1.69 s、1.58 s、1.84 s和3.09 s,相比Mean-shift和Otsu算法,平均计算效率分别提升150%和3%,并且图像中的背景杂波、目标阴影和干扰小目标均被有效去除。结论 综合利用边界和纹理信息能够有效抑制相干噪声,去除图像斑点和小目标的干扰,从而达到目标边界的连接,实现对SAR图像目标的自动分割。实验结果表明,本文算法可以满足工程化应用要求,自适应性强,分割精度高,且具有较好的鲁棒性。     

A novel STAP method for the detection of fast air moving targets from high speed platform

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar.However,the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets.It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters.However,in the presence of serious Doppler ambiguity of fast air moving targets,distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets,and as a result the STAP performance will degrade.In this paper,a novel STAP method is proposed for the detection of fast air moving targets from high speed platform,which can deal with the range walk of both clutter and targets.Effectiveness of the new method is verified via simulation examples.     

基于聚类的SAR图像快速目标检测

针对SAR图像目标检测效率低、虚警概率高及SAR图像的特点,改进了Mean Shift聚类算法,并与双参数CFAR检测技术相结合,提出了一种能够快速而准确的SAR图像目标检测算法。通过聚类预处理SAR图像,降低了背景杂波对目标检测的影响及检测的虚警率,并且聚类后的SAR图像具有一定的结构,将图像结构的概念引入到目标检测中,避免了对图像逐点检测,大大提高了检测速度。实验结果表明,该方法具有检测速度快、虚警概率低的特点。     

K近邻优化估计的SAR图像建模与目标检测算法

在非均匀杂波环境下的合成孔径雷达(synthetic aperture radar,SAR)图像背景建模问题中,针对非参量建模算法Parzen窗估计严重依赖于窗宽设置及最优核函数选择的问题,提出一种基于K近邻优化的概率密度函数估计算法,解决因固定近邻数而导致估计不准确甚至不能估计的问题.该算法不需要图像的任何先验知识,且无需考虑窗宽的设置及最优核函数的选择问题.与Parzen窗估计、K分布和$G^0$分布的对比实验表明,所提出的K近邻优化估计算法可以实现对单峰、多峰甚至不规则图像数据的准确建模,优于K分布和$G^0$分布;同时,对图像首尾数据的处理优于Parzen窗估计.实验结果验证了所提出方法对SAR图像杂波建模的精确性、鲁棒性和简便性,以及全局恒虚警率目标检测的有效性.     

遥感异常目标的仿生非线性滤波检测

目的 为了解决复杂背景干扰下基于线性滤波异常检测算法无法有效区分复杂背景特征与异常目标特征,导致检测结果虚警率偏高的问题,提出一种面向复杂背景的遥感异常小目标仿生非线性滤波检测算法。方法 受生物视觉系统利用不同属性信息挖掘高维特征机理的启发,该算法通过相关型非线性滤波器综合多波段光谱数据提取高维光谱变化特征作为异常目标检测检测依据,弥补线性滤波抗噪性能差,难于区分复杂背景特征与目标特征的缺点。结果 仿真实验结果验证该算法在仿真数据及真实遥感数据的异常检测效果上有较大改善,在实现快速异常检测的同时提高了检测命中率。结论 本文方法不涉及背景建模,计算复杂度低,具有较好的实时性与普适性。特别是对复杂背景下的小尺寸异常目标具有较好的检测效果。     

利用直接数据域的双基机载雷达地面动目标检测方法

双基机载雷达的杂波存在严重的距离依赖性,从距离上获得的样本不能够准确估计杂波加噪声协方差矩阵,从而导致空时自适应处理(STAP)的杂波抑制性能严重下降。针对该问题,提出了利用直接数据域(DDD)的双基机载雷达地面动目标检测方法。因为DDD算法是在单个距离门对目标滤除后进行空时平滑来获得足够多的样本,所以不需要对双基雷达杂波距离依赖性进行补偿。仿真实验验证了该方法的有效性。