红外单站被动定位技术速度更新算法研究

针对红外单站被动定位技术中空中机动目标的运动速度不断变化这一实际情况,提出了一种基于红外相机运动特性的对目标的运动速度进行实时更新的校正算法.该算法克服了大多基于匀速直线运动这一空间模型的红外单站被动定位方案目标速度不变的缺点,客观地反映了空中机动目标的实际运动状况,不仅能对目标的运动速度进行实时准确的更新校正,而且可推演出一种新的红外单站被动定位算法,对推动红外单站被动定位技术的工程实用化进程具有重要的意义.     

一种改进的红外图像归一化互相关匹配算法

分析了传统归一化互相关算法在红外空中目标匹配定位时失效的原因,提出一种改进的红外图像归一化互相关匹配算法.该方法将模板和匹配区域之间的纹理相关计算看作一个最优化问题,寻求使图像纹理相关匹配鲁棒性最好的相关基准值,用图像的相关基准函数替代传统方法中的区域均值部分,构造了一种适用于的红外目标匹配的归一化相关算法.实验结果表明,该相关匹配算法对模板中背景部分的变化和非均匀性亮度变化有良好的抗干扰能力,较好地解决了恶劣环境下红外对空目标跟踪中匹配定位出错的问题.     

红外鱼眼系统下的多目标威胁评估研究

针对红外鱼眼告警系统在对几公里以外的目标成像时表现为点目标,无距离、几何形状和纹理信息可以利用,很难对来袭目标的威胁程度做出较准确的评估问题,研究了红外鱼眼系统下的多目标威胁评估排序方法。提出了一种红外鱼眼系统下的多目标威胁评估模型。该模型利用激光测距机所获取的各目标的初始距离信息推导出了目标在各时刻的距离和径向速度,从而建立起了以目标的距离、径向速度、航向角和高低角为威胁指标的多目标威胁评估模型。然后根据多目标威胁评估的非线性特点,以及神经网络在解决非线性复杂问题所具有的良好的自适应能力和自学习能力,利用径向基函数(RBF)神经网络对多目标威胁程度进行评估并讨论了训练样本的生成方法。实验结果证明了该方法的可行性和有效性。     

无人机空中光轴基准安装误差及磁偏差测量方法研究

测定飞机与目标定位有关的机载设备的安装误差、磁偏角等固定误差,是无人机实际目标定位前必做的一项工作.现有测定、校正方法繁琐且实用性差.给出一种不同于现有工序,在无人机空中实际目标定位前,在空中利用飞行器本身光电平台的光轴作为基准,对预先测定好经纬度的地面目标进行定位,反向推算来测量出飞机的固定安装误差及磁偏角,再进行校...     

飞机尾焰红外图像采集与分析

介绍了超音速机载红外测量吊舱系统(以下简称红外测量吊舱)中红外探测器的选型,以及在测量目标机亚音速飞行时飞机尾喷口及飞机尾焰的电视和红外热图,并对跟踪空中飞行目标所采集到的红外尾焰热图作了细致的分析和计算,从而为空中目标的红外特性检测分析、研究提供了有效的数据积累.     

基于能量分布的红外像点定位分析方法

在红外探测器的应用中,常用统计分析、算例验证等方法研究目标成像精确定位问题。鉴于上述方法难以充分阐释物理意义与揭示普遍规律,提出基于能量分布及成像特征的分析方法,针对红外探测目标成像精确定位问题,利用能量分布和数字图像中成像规律,研究像点定位及精度分析。研究了红外目标成像和数字图像特点,建立了定位模型和方法流程;分析了定位误差影响因素,实现了定位结果精度评定;最后结合典型应用实例,进行了计算验证。该方法与已有方法相比,分析过程更直观,获取了红外像点定位分析的理论依据,并给出了定位结果的精度水平：通常红外成像应用中,精度优于1/6像元;在成像较大时,精度可达1/10像元以上。该研究结论对红外探测应用中目标准确定位具有重要意义。     

基于加权mean-shift可见光/红外双通道目标跟踪

为提高目标亮度突变时的跟踪性能,在每一帧进行目标跟踪时,首先提取可见光图像的颜色特征,红外图像的垂直投影图像和水平投影图像特征,然后利用可见光/红外各自通道的Bhattacharyya系数计算该通道的权值,并对加权mean-shift双通道跟踪方法进行了推导,提出了基于加权mean-shift可见光/红外双通道目标跟踪算法.该方法使前后两帧目标相似度大的通道取大的权值,从而达到有效利用各通道有利信息、提高跟踪性能的目的.实验表明,用本文提出的算法进行可见光/红外双通道目标跟踪时,与基于mean-shift单通道(可见光或红外)目标跟踪算法相比,可提高目标跟踪的准确度,特别是当目标进入树荫区域,引起目标亮度发生显著变化时,跟踪性能基本不受影响.     

基于修补粒子群算法的红外目标跟踪

为了避免粒子群算法退化和运算量大问题,提出利用修补粒子群算法对红外目标进行跟踪。该算法先用设置粒子的惯性因子对搜索到的红外目标位置进行修正,使粒子的位置达到局部最优点和全局最优点;然后通过粒子群收缩因子限制在边界搜索,消除目标位置的模糊性。利用该方法对空中红外战斗机图像跟踪仿真,结果显示在500次粒子迭代,100次跟踪中误差为2.83%,在最大惯性权值为1.2和最小惯性权值为0.3时跟踪效果最接近真实目标,且边缘最清晰。     

曲率限制的红外焦平面告警系统目标搜索算法

为了降低红外焦平面告警系统中较高的虚警率,提出了一种速度限制基础上基于曲率限制的目标搜索算法。以视场角5.34.0凝视型红外告警系统为例,在MATLAB中对红外图像序列进行了目标搜索实验,验证了该算法的可行性。按照蒙特卡罗法思路模拟了包含大量随机噪声的目标场景,研究了该算法相比传统多帧检测算法对随机噪声的抗干扰性。对该算法的搜索帧数进行了扩展,给出了不同搜索帧数的搜索效果和时效性表现。     

跟踪、瞄准、光电对抗设备

提出了对空中点目标进行被动测距的方法。基于红外辐射在大气中的传输和红外成像系统的特性,建立了目标和背景的表观对比度模型。通过对两个波段上对比度模型的分析,推导出了测距公式和距离分辨率公式。理论分析表明,只要得到大气在两个波段上的消光系数,通过测量目标在两个波段、两个时刻上的目标表观对比度,即可计算得     

红外序列图像弱小动目标检测的研究

针对云天背景下的红外运动弱小目标,提出了一种融合检测的方法。根据视觉的固视微动特性进行单帧图像的目标增强,利用帧差分方法检测多帧图像的动目标,最后将这两种处理结果进行基于调制的融合,并经过图像分割,从而达到动目标检测的目的。仿真结果表明该检测方法能够有效地实现复杂背景下的弱小动目标检测。     

自动选取辅助目标的建筑物目标间接定位方法

针对序列图像中目标因不显著、被遮挡等情况而出现的虚警或丢失问题，提出一种基于自动选取辅助目标的建筑物目标间接定位方法，提高目标跟踪稳定性。该方法首先自动选取红外图像中形状稳定、灰度显著的区域作为辅助目标。定位建筑物目标后，提取其与目标相对位置关系, 之后利用该相对位置间接定位，最后融合直接识别及间接定位结果定位目标，并能实时更新辅助目标，保证其时刻处于视场中。实验结果表明:通过间接定位能解决目标虚警或丢失问题，提升目标定位过程中算法的准确性及鲁棒性。     

相邻帧间匹配的迎头点目标跟踪算法

针对海空复杂背景下迎头点目标检测与跟踪难题,提出了一种基于相邻帧间匹配的边检测边跟踪算法.算法对相邻红外图像序列帧间点与点的邻域匹配,标记匹配结果兴趣区域像素点,统计标记次数,与输入单帧图像同步显示迎头目标检测结果.算法主要特点在于无需提前假定疑似目标点位置,单个匹配过程与当前相邻两帧外的其它序列帧无关,整个匹配过程不随目标数目多少或运动状态变化而改变.根据仿真和实拍照片实验,证实了理论上区别于传统算法的上述优点,在军事应用中具有较高的参考价值.     

Detectability of infrared small targets

Selecting the most appropriate algorithms for detecting small targets in varied infrared image scenes is frequently needed, since the relative characteristics between small targets and backgrounds in varied scenes are disparate. To solve that problem, a novel criterion is proposed in this article to measure the difficulty in distinguishing small targets from infrared images, which is named detectability of infrared small targets (DIST). Then a new method to detect small targets based on it was introduced. Detection probability and false alarm probability were employed to evaluate the performance. The results from the experiments further demonstrated the robustness of the proposed methodology under different backgrounds.     

Infrared small target detection based on directional zero-crossing measure

Infrared small target detection under complex background and low signal-to-clutter ratio (SCR) condition is of great significance to the development on precision guidance and infrared surveillance. In order to detect targets precisely and extract targets from intricate clutters effectively, a detection method based on zero-crossing saliency (ZCS) map is proposed. The original map is first decomposed into different first-order directional derivative (FODD) maps by using FODD filters. Then the ZCS map is obtained by fusing all directional zero-crossing points. At last, an adaptive threshold is adopted to segment targets from the ZCS map. Experimental results on a series of images show that our method is effective and robust for detection under complex backgrounds. Moreover, compared with other five state-of-the-art methods, our method achieves better performance in terms of detection rate, SCR gain and background suppression factor.     

基于多特征融合的复杂背景下弱小多目标检测和跟踪算法

针对红外弱小多目标的检测和跟踪难题,提出一种基于多特征融合的复杂背景下弱小多目标检测和跟踪算法.融合红外弱小运动目标的灰度特征、梯度特征、运动特征等多个典型特性,进行复杂背景下弱小多目标的检测和跟踪.实验证明：该算法应用于复杂背景下低信噪比的红外弱小多目标图像序列能得到较理想的结果,算法检测概率高、检测速度快、具有较强鲁棒性.     

Hybrid multi-resolution detection of moving targets in infrared imagery

A hybrid moving target detection approach in multi-resolution framework for thermal infrared imagery is presented. Background subtraction and optical flow methods are widely used to detect moving targets. However, each method has some pros and cons which limits the performance. Conventional background subtraction is affected by dynamic noise and partial extraction of targets. Fast independent component analysis based background subtraction is efficient for target detection in infrared image sequences; however the noise increases for small targets. Well known motion detection method is optical flow. Still the method produces partial detection for low textured images and also computationally expensive due to gradient calculation for each pixel location. The synergistic approach of conventional background subtraction, fast independent component analysis and optical flow methods at different resolutions provide promising detection of targets with reduced time complexity. The dynamic background noise is compensated by the background update. The methodology is validated with benchmark infrared image datasets as well as experimentally generated infrared image sequences of moving targets in the field under various conditions of varying illumination, ambience temperature and the distance of the target from the sensor location. The significant value of F-measure validates the efficiency of the proposed methodology with high confidence of detection and low false alarms.     

小目标红外图像背景噪声的抑制及方法讨论

提取复杂背景条件下目标红外信号的方法有很多,而采用背景噪声抑制的方法是检测目标红外信号的一种有效方法。介绍了抑制小目标红外图像背景噪声的三种方法：空域高通滤波法、频域高通滤波法和自适应门限背景抑制法,并对各种方法的抑制结果进行了验证与分析。     

A novel infrared small moving target detection method based on tracking interest points under complicated background

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.