基于约束序贯M估计的时空域融合红外杂波抑制

针对红外弱小目标检测中的强背景杂波干扰抑制问题进行研究,提出了一种基于参数约束序贯M估计的时空域融合自适应杂波抑制算法.该算法首先在分析序列图像帧间失配的基础上建立了一种改进的时空域融合背景预测模型,结合二维离散傅里叶快速变换图像配准和双线性插值方法进行灰度值估计;然后,基于约束序贯M估计方法进行模型参数的自适应估计,...     

基于空域特性的低空空域雷达目标检测

为实现基于非相参雷达的低空空域监视,提出一种基于空域特性的杂波抑制算法,通过构造"最优分类面"来区分复杂低空空域雷达图像中的小弱目标和杂波,极大改善了非相参雷达的低空探测能力。首先,采用背景差分与固定阈值分割建立前景和背景统计模型。然后,基于该模型提取空域特性,建立马尔可夫随机场模型,从而自适应地调节"最优分类面"中的阈值。前景模型数据反映了待检测像素的聚集程度,背景模型数据反映了其相对位置。将本算法分别应用于X波段和S波段航海雷达获取的图像序列。检测结果表明:本算法在检测到低空空域小弱目标的同时,能够将虚警率保持在较低的水平,优于恒虚警检测等经典算法。最后,将本算法与已经实现的目标跟踪算法相结合,实现了一整套完整的低空空域雷达目标检测与跟踪算法。     

地基观测红外目标二维经验模式分解检测方法及应用

针对复杂条件下的地基观测红外图像目标检测问题,引入二维经验模式分解(BEMD)方法对复杂条件下的目标红外实测图像进行了处理分析:首先对目标观测原始红外图像进行中值滤波,对原始红外图像进行噪声抑制预处理;然后利用BEMD算法对预处理后红外图像进行自适应分解,获得按频段分布的二维基本模式分量与残余图像,并对分解后的红外图像进行有效重构,获取处理后的红外目标图像;最后利用点锐度方法定量评估目标红外图像处理效果,并与原始红外图像进行比对分析。基于实测红外图像处理结果表明,BEMD方法有效抑制了云层背景杂波噪声,且有效检测出清晰的红外目标,处理后的红外图像清晰度较原始红外图像显著提高。     

基于子空间投影的未知背景航拍高光谱图像恒虚警目标检测

针对航拍高光谱图像中未知背景地物特征条件下小目标的检测问题,给出一种检测算法。利用目标的低概率特性,通过模糊聚类获取高光谱图像中背景的光谱特性;然后将高光谱数据向背景光谱信号的正交子空间及目标信号子空间投影以抑制背景和噪声信号;最后在特征层利用广义似然比检验构造出具有恒虚警特性的检测器,完成融合检测过程。理论分析和实验结果表明了算法的有效性。     

基于背景补偿技术的运动目标检测研究

基于运动补偿技术,研究了动态背景视频中运动目标的检测。首先,介绍了几种视频成像模型;然后,选取六参数投影模型,结合基于图像灰度的参数估计方法和基于图像特征的参数估计方法,运用Levenberg-Marquardt算法进行图像运动参数的估计;最后,对补偿后的当前帧和参考帧作差分检测出运动目标。通过Matlab仿真试验验证了本文算法的有效性。     

基于边缘检测与颜色目标定位的图像检索

提出一种先利用图像边缘信息缩小检索范围。再利用图像颜色信息进行目标定位的图像检索算法。首先。提取图像边缘信息。利用图像位错率进行边缘信息比较,位错率越小图像边缘信息越接近;然后,利用主色调确定图像的目标块,进行目标子块间的比较。算法忽略图像中不重要的背景因素,有效利用重要的目标因素。从而提高了检索效率。仿真实验证明该算法比单一使用边缘检测、颜色目标定位具有更好的检索性能。     

基于杂波子空间估计的MIMO雷达降维STAP研究

 多输入多输出(MIMO)雷达是近年来出现的一种新体制雷达,针对MIMO体制的机载雷达开展空时自适应处理（STAP）技术研究是值得进一步努力的方向。本文研究了机载MIMO雷达STAP技术的降维算法,通过对STAP技术杂波抑制原理进行分析,推导并得到一种基于杂波子空间的降维算法。结合扁长椭球波函数（PSWF）的特点,提出了一种基于杂波子空间估计的降维算法,并与若干降维算法的杂波抑制性能进行比较。结果表明,当存在阵元幅相误差时,该算法在保持杂波抑制性能的同时能够有效地降低STAP算法的运算量。     

基于非线性自适应滤波器的海杂波抑制技术

海杂波会严重影响雷达对海洋表面目标的检测。为了提高海洋表面目标的检测性能,基于非线性自适应滤波器,提出了海杂波抑制方法,实现了海杂波抑制。介绍了非线性自适应滤波器的原理以及利用梯度下降法训练滤波器的方法。利用IPIX雷达实测海杂波数据,分别针对岸基雷达和机载雷达,进行了此方法的仿真试验,从均方预测误差(MSE)和信杂比改善因子(IF)两个方面,分析了此方法的性能。并与基于线性预测的LMS算法进行了比较,得出了此方法对慢速目标的检测性能优于线性方法。     

航拍视频帧间快速配准算法

 为应对相机运动的影响,提出了一种快速有效的无人机(UAV)视频相邻帧图像配准算法。通过空间分布约束和角点量限制来筛选有效的FAST特征点,引入自适应阈值提高特征点检测的环境适应性,采用训练得到的不相关采样点集对特征点进行二值描述,以获得准确快速的特征描述,并通过最近邻算法根据汉明距离获得特征匹配对,最后运用RANSAC方法得到帧间仿射变换模型参数,消除相机运动带来的影响,为后续运动目标检测与跟踪提供保障。实验结果表明该算法快速、稳定,具有较高的环境适应性,能够满足无人机系统视频图像配准的要求。     

一种基于背景自适应的运动目标检测与跟踪算法

背景图像差分法是运动目标实时检测中常用的方法,但缺乏背景图像随监视场景光照变化而及时更新的合理方法,限制了该方法的适应性。对此,文章首先提出了一种自适应背景更新方法;然后利用最大类间方差法实现运动目标的自适应阈值分割,并利用基于形态学方法的连通区检测算法检测运动目标;最后以Kalman滤波为运动模型实现对运动目标的连续跟踪。实验结果表明:所提方法可随着光照条件的变化,实时、准确地检测出运动目标并实现稳定跟踪。     

机载MIMO雷达空时自适应杂波对消器

针对机载多输入多输出(MIMO)雷达杂波分布呈现空时耦合特性,提出一种空时自适应杂波对消器.利用机载MIMO雷达的脉冲回波数据,构造杂波对消器的系数矩阵.通过空时自适应杂波对消器的预处理,可以有效地抑制杂波,并通过与常规空时处理算法的级联,最终可以有效提高动目标的检测性能.实现了由传统地基雷达杂波对消器向机载运动平台的推广.仿真结果表明,这种自适应杂波对消器不仅适用于正侧视雷达,对于非正侧视雷达也同样适用.     

Sum-difference Reduced-dimensional Processing in SAR Image Domain and Statistical Analysis for Ground Moving Target Indication

In this article, a new reduced-dimensional adaptive processing algorithm based on joint pixels sum-difference data for clutter rejection is proposed. The sum-difference data are obtained by orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multi-satellite radar system. In the sense of statistical expectation, the sum-differ- ence data contain the common and different information of the SAR images. Therefore, the objective of clutter cancellation can be achieved by adaptive processing. Moreover, based on the residual image after clutter rejection, statistical analysis of constant false-alarm rate (CFAR) detection of moving targets is also presented. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm even with heterogeneous clutter and image co-registration error.     

Optimal speckle reduction in polarimetric SAR imagery

Speckle is a major cause of degradation in synthetic aperture radar (SAR) imagery. With the availability of fully polarimetric SAR data, it is possible to use the three complex elements (HH, HV, VV) of the polarimetric scattering matrix to reduce speckle. The optimal method for combining the elements of the scattering matrix to minimize image speckle is derived, and the solution is shown to be a polarimetric whitening filter (PWF). A simulation of spatially correlated, K-distributed, fully polarimetric clutter is then used to compare the PWF with other, suboptimal speckle-reduction methods. Target detection performance of the PWF, span, and single-channel |HH|² detectors is compared with that of the optimal polarimetric detector (OPD). A novel, constant-false-alarm-rate (CFAR) detector (the adaptive PWF) is as a simple alternative to the OPD for detecting targets in clutter. This algorithm estimates the polarization covariance of the clutter, uses the covariance to construct the minimum-speckle image, and then tests for the presence of a target. An exact theoretical analysis of the adaptive PWF is presented; the algorithm is shown to have detection performance comparable with that of the OPD     

Stap using knowledge-aided covariance estimation and the fracta algorithm

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

基于先验门限优化准则的探测阈值自适应选择

针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57     

Adaptive Rectification Filter for Detecting Small IR Targets

Clutter suppression is one of the most important subjects in the field of small target detection under infrared (IR) strong clutter background. While removing the clutter background, however, such methods may reverse the relative energy distribution of target and noise in the clutter suppressed image, and disturb the subsequent target segmentation and detection. This paper analyzes the causation of such problems, does research on the relationship between target energy characteristics and detection probability, and presents a novel filter of energy distribution adaptive rectification (EDARF). Based on the EDARF, an improved framework of dim small target detection is proposed to rectify the energy distribution in the clutter-suppressed images by conventional adaptive filters. The proposed EDARF's performance is estimated by experimental comparisons of three linear/nonlinear filters before and after using EDARF. Extensive experimental results show that the proposed EDARF improves efficiently the performance of detecting dim small targets against strong undulant cloud-cluttered backgrounds.     

无需辅助数据的分布式目标自适应检测器

在非高斯背景和没有辅助数据的条件下,研究了高分辨率雷达分布式目标的自适应检测问题.首先采用有序检测理论和协方差矩阵的迭代估计方法粗略估计散射点集合,进一步利用迭代估计方法获得协方差矩阵的近似最大似然估计,提出了无需辅助数据的自适应检测器(ADWSD).ADWSD在非高斯背景下具有近似恒虚警率特性,且检测性能远好于修正的...     

红外极小目标检测算法研究

低信噪比检测技术是实现红外自动目标识别的基本前提,其性能指标将直接决定系统探测距离的远近,是反映红外低可观测目标识别能力至关重要的一项核心技术。自适应背景估计方法是实现这一目标的有效途径。本文在重点论述几种背景估计常用技术的基础上,提出了红外极小目标的形态滤波优化改进算法,通过理论分析和实验检测表明：该算法简化了形态变...