Infrared modeling and imaging simulation of midcourse ballistic targets based on strap-down platform

An infrared （IR） imaging simulation framework based on the strap-down platform is proposed for midcourse ballistic targets. It overcomes the shortcoming of the existing algorithms, which cannot simulate IR imaging from the entire midcourse process. The proposed framework includes three steps, target characteristic modeling, motion modeling, and imaging modeling. In imaging modeling, the staring focal plane is taken into account due to its wide employment. In order to obtain IR images of high fidelity, especially that the fluctuation of the target signal-to-noise ratio （SNR） is reasonably similar to the actual one, this paper proposes an improved IR imaging simulation method. The proposed method considers two critical factors of the pixel plane, occupy-empty ratio and defect elements, which affect the imaging of targets markedly but are neglected in previous work. Finally, the IR image sequence of high fidelity is obtained. And the correlative parameters of simulation can be set according to the given scene. Thus the generated images can satisfy the needs of algorithms validation for tracking and recognition.     

Novel imaging methods of stepped frequency radar based on compressed sensing

The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Experiments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier transform method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.     

Coded aperture compressive imaging array applied for surveillance systems

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio （SNR） is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic （ROC） curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.     

Research on infrared dim and small target detection algorithm based on low-rank tensor recovery

In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor r...     

基于视景通道的透镜成像过程仿真技术研究

针对计算机成像模型在使用过程中存在的不足,提出了对该模型进行完善与改进的方法。首先根据数字变焦原理,推导出放大倍率与视锥角的关系公式,实现了视景通道的变焦功能,该方法计算量小,效果逼真;其次,提出了一种镜头对焦过程的仿真方法,它充分利用粒子系统技术模拟对焦成像由模糊到清晰的过程,不仅获得更真实的图像效果,且满足实时性要求。上述改进后模型与实际摄像系统原理基本一致,增强了仿真系统视觉效果逼真性与沉浸感。由于该方法在工程上极易实现,可以在虚拟现实系统中广泛应用。

Abstract：

A way that could be improved was brought forward on the pin-hole camera model in allusion to the problems of the insufficient cue of depth. The relation formulas of the magnification and aspect angle were concluded according to the principles of digital zoom and the zooming function of visual channels was realized. The implementation of this method is simple and the effect is realistic. Besides,a simulation method for the course of adjusting focus was proposed,which brought blurry images into focus by particle system. Both vivid images and real time requirement could be achieved. The ameliorated model runs in the same way as the real camera system on the theory,thus enhancing the fidelity and immersion of visual effects. It is so easy to implement in project that this approach can be used universally in VR systems.     

复杂背景下基于改进粒子滤波的红外人体跟踪

为解决复杂背景下红外图像序列中的人体跟踪问题,提出了一种改进的粒子滤波跟踪方法。根据红外图像中人体目标的特点,首先建立人体的灰度直方图来提取其灰度特征,同时采用一种新的基于帧间差分和灰度概率分布图的方法提取其运动特征。然后将上述两种特征融合到粒子滤波框架中,用于粒子权值的计算,最终实现红外序列中人体的稳健跟踪。实验结果表明,和传统粒子滤波算法相比,该方法大大提高了复杂背景下红外人体跟踪的准确性和有效性,跟踪结果令人满意。

Abstract：

An improved particle filter tracking algorithm was proposed to solve the problem of human tracking in infrared image sequences under complex background.According to the characters of the human in the infrared images,the algorithm firstly constructed the gray histogram of the human to extract the gray feature,and at the same time utilized a new method based on the inter-frame difference and gray probability distribution image to get the motion feature.Then,the above-mentioned two features were fused into the particle filter frame to calculate the particle weights.Finally the robust tracking of human in infrared image sequences was achieved.The experimental results show that compared with the traditional particle filter algorithm,the presented method greatly improves the accuracy and effectiveness of the infrared human tracking under complex background,and the tracking results are satisfactory.     

New CFAR target detector for SAR images based on kernel density estimation and mean square error distance

A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error (MSE) distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm (PFA), an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.     

Distribution-based CFAR detectors in SAR images

1. INTRODUCTION Many researches have been made on automatic target recognition (ATR) from SAR images in the past few years. Lincoln Laboratory has developed a complete, end-to-end ATR system[1-4]. The whole system inclu- des three stages: detection (or prescreening), discri- mination and classification. In the detection stage, a two-parameter CFAR (constant false alarm rate) detector is used as a prescreener to select candidate targets in an SAR image on the basis of local brightne…     

Angular extent effect of micromotion target in SAR image by polar format algorithm

Target micromotion not only plays an important role in target recognition but also leads to esoteric characteristics in synthetic aperture radar （SAR） imaging. This paper finds out an interesting phenomenon, i.e. the angular extent effect, in micro-motion target images formulated by the polar format algorithm. A micromotion target takes on multiple pairs of paired echoes （PEs） around the true point, and each PE extends for an angle which is exactly equal to the angular extent of the synthetic aperture, regardless of the micromotion frequency. The effect is derived and interpreted by using the characteristics of Bessel functions. Then it is demonstrated by simulation experiments of a target with different micromotion frequencies. The revelation and interpretation of the effect is highly beneficial to micromotion-target SAR image understanding as wel as target recognition.     

An approach of motion compensation and ISAR imaging for micro-motion targets

Inverse synthetic aperture radar(ISAR)imaging of the target with the non-rigid body is very important in the field of radar signal processing.In this paper,a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain,and the micro-Doppler(m-D)signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition(CEMD)algorithm,which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center.Then,a better focused ISAR image of the target with the non-rigid body can be obtained consequently.Results of the simulated and raw data demonstrate the effectiveness of the algorithm.     

红外成像制导半实物仿真系统研究

红外图像的生成是红外成像制导半实物仿真试验中的一个关键技术.设计了一个红外成像制导半实物仿真系统,利用Multigen Creator建立目标和背景的三维模型,利用Vega中基于大气传输模型的SensorVision模块建立目标与背景的红外模型,生成目标及场景的红外图像.并将该红外图像应用于实际的某红外成像半实物仿真试验系统中,试验效果良好,达到预期目标.     

基于玫瑰线扫描双色信息融合目标识别方法

图形扫描复合制导是当今深受国内外重视的一种制导方式。虽然玫瑰线扫描双色导引头技术已在战术地空导弹中得到了应用,但目前主要将其作为一维信号加以处理,不利于目标的识别与信息融合。本文的基本思想在于将玫瑰线扫描得到的一维波形转化为二维准成像图像,同时引入目标、背景以及诱饵的红外与紫外概率模型,采用Ｄｅｍｐｓｔｅｒ证据理论将红外和紫外准成像图像进行融合,以提高系统去除诱饵即假目标的能力。模拟实验结果证明了本文所提出方法的有效性和可行性。     

虚拟环境红外物理模型研究

分析了影响自然环境和人造建筑红外特性的各种因素，按照红外辐射的基本规律，建立了红外物理模型，环境的红外辐射图像和可见光图像是密切相关的，增添材料和气候信息，配合视景数据库中的环境，实体几何模型，可计算出观测环境的辐射亮度，最后转化为探测器图像，本文方法可提供包括自然背景，人造建筑在内的虚拟环境的约年探测器图象的显示，适用于动态变化的环境和目标。     

基于图像灰度熵的团块目标检测方法

针对面向自动目标识别(ATR)的图像数据压缩方法,感兴趣区域(ROI)检测器的设计是需要解决的一个关键问题。图像灰度熵可以有效地反映图像中像素灰度变化的剧烈程度,并且与图像均值有关。提出了一种基于图像灰度熵的团块目标检测方法,实现了对可见光和红外图像中坦克、飞机、舰船等团块目标的有效检测。实验表明该方法具有较好的稳健性和适应性,优于基于分水岭的检测方法,满足面向ATR图像数据压缩方法ROI检测器的设计要求。     

基于顺轨-交轨InSAR技术的运动舰船目标三维成像

利用顺轨和交轨干涉SAR技术,研究了海面运动舰船目标三维成像问题.完整地介绍了距离徙动校正和二维图像配准方法,提出了将时频分析成像方法和干涉技术相结合的处理思路,利用时频分析方法可以获得三个天线在任意方位时间tk处的目标图像,将任意方位时刻tk处的二维复图像进行干涉处理,由干涉相位计算运动目标散射点的三维空间坐标,实现运动舰船目标三维成像.运动舰船目标的三维图像仿真处理结果,表明了该方法的有效性.     

基于多域网络的红外目标跟踪算法

随着红外探测器性能的不断提升, 红外目标跟踪在智能安防等领域的应用越来越广泛。然而红外图像分辨率较低, 依赖其进行目标跟踪仍然是一个制约相关应用发展的难题。针对红外图像分辨率低的特点, 以多域网络为模型算法框架, 结合目标运动过程中的尺寸变化特点, 提出了一种基于多域网络的红外目标跟踪算法。为了评估算法性能, 分别在VOT-TIR2016数据库和AMCOM红外数据上进行了实验。实验结果表明, 目标尺度预测机制能够显著提高算法的跟踪精度。     

基于信号注入仿真的红外成像导引算法评估系统

在红外成像导引算法性能评估中,信号注入式仿真不用将真实导引头引入闭合回路。计算机成像系统模拟战场环境,产生包含目标、背景和干扰的红外图像源,通过视频输出接口注入到图像处理器,图像处理器按照成像导引头的工作方式,对目标进行捕获跟踪。以反舰导弹为仿真对象,介绍了系统的构成,阐述了以提取天水线为准则的图像分割算法和模糊综合评判目标识别算法,分析了形心和相关两种跟踪算法的适用场合和工作过程,最后给出了评估准则。     

基于图像仿真的对地遥感过程科学可视化研究

分析了成像链的光谱辐射响应和空间响应特性，提出一种基于图像仿真的对地遥感过程科学可视化方法。文中建立了成像链分析模型，讨论了传感器获取的三维场景像元辐幅度、探测器输出信号、随机噪声和调制传递函数等数学模型，并导出了包含传感器综合响应特性的遥感数字图像传输方程。此外，对专业软件RSIS1．0可见近红外波段的仿真模块、数据库及仿真过程作了简要介绍。最后，给出了部分仿真遥感数字图像。     

数目可调型ISAR图像欺骗干扰技术研究

将间歇采样转发处理和散射波干扰方法相结合,提出了一种针对逆合成孔径雷达(inverse synthetic aperture radar, ISAR)的虚假目标图像数目可调型欺骗干扰方法。推导并对比了雷达目标回波和散射波干扰信号在目标二维ISAR图像重构时的差异,分析了散射波干扰后虚假图像的旋转现象;结合解线频调处理,分析了间歇采样占空比、采样周期对干扰效果的影响,得到了虚假图像数目与成像场景大小、采样周期之间的约束关系,从而实现虚假目标图像数目可调。数字仿真和实测数据实验均证明了该方法的有效性。     

红外双色亚图像识别研究

研究了红外双色辐射特性和双色比识别特征 ,分析了红外亚成像的灰度与空域特征。研究了双色比区分伪目标 ,灰度扩展亚成像方法。研究了亚图像子块灰度直方图的迭代二值化 ,二值分割后的空域模糊分割。研究了单元扫描亚图像识别的特征参数 ,初始成像多帧配准识别实现低虚、漏警率目标检测 ,并通过锁定目标实施连续逐帧分割、识别。实验表明 ,所研究的红外双色亚成像识别方法适用于静止、慢速和快速红外目标的实时识别