红外成像导引头抗干扰性能评估仿真研究

针对日益复杂的战场对抗环境,导引头抗干扰能力已成为非常重要的指标,红外成像制导武器因具有高命中率、高效费比及强抗干扰能力而广泛应用。随着红外成像导引头识别跟踪算法的蓬勃发展,红外成像导引头的抗干扰能力已经不能简单以武器打击精度为单一的评估手段,文中提出了一种基于目标识别、图像特征、跟踪精度的红外成像导引头抗干扰能力评估体系,通过构建仿真对抗场景,对红外成像导引头抗干扰性能进行仿真,仿真结果验证了该方法的合理性和有效性。     

红外成像导引头随动系统建模及仿真

目前红外成像制导武器的随动系统均采用单通道双环（速度环、位置环）结构,对结构虽简单可靠,但也影响了随动系统的响应速度和导弹的跟踪性能,为分析论证成像导引头随动系统采用单通道三环控制的可行性及优越性,建立了单通道三环（电流环、速度环、位置环）控制的随动系统模型,对单通道三环和单通道双环两种模型的时域性能进行了仿真研究,仿真结果表明单通道三环模型与双环模型在稳态误差相同的情况下,前者的动态性能更好,意味着采用单通道三环的成像导引头随动系统会有更快的响应速度。通过分析和建模为成像导引头实现单通道三环嵌套随动系统工程化提供了理论支持。     

红外成像导引头抗干扰性能评价指标体系

目前,各类红外成像制导导弹面临的主要问题是抗干扰能力的强弱,包括自然环境干扰和人工干扰两种;因此红外导引头的抗干扰性能成为各类红外制导导弹的关键性能,极大地影响到导弹的作战性能;为了能够合理的评价红外成像导引头的抗干扰性能,就必须制定合理的红外成像导引头抗干扰性能评价指标;为此,笔者根据红外成像导引头的抗干扰工作过程及其特点,以及传统的评价指标,并借鉴深度学习的评价指标,提出了由截获能力、识别能力、跟踪能力和命中精度能力这四方面组成的抗干扰性能评价指标体系;明确了四项指标下的二级指标内涵和计算方法,根据红外仿真与数据处理结果表明,这4种评价指标均可以有效地体现出导引头抗干扰性能的强弱,而且识别能力和命中精度这两个指标更加突出,故笔者所提出的红外成像导引头抗干扰性能指标体系及其计算方法,均可成为红外成像制导导弹抗干扰性能评估的判断依据.     

基于BP神经网络的捷联导引头控制系统设计

滚仰式捷联导引头稳定平台控制器的设计,是导引头完成闭环控制对机动目标实现快速稳定跟踪的关键.导弹在对目标进行跟踪攻击时弹体本身常常处于复杂的运动状态,这就对滚仰式捷联导引头的稳定控制产生严重影响.针对这一问题,提出了一种基于BP神经网络的PID控制器用于滚仰式捷联导引头位置回路校正环节,建立了导引头稳定与跟踪一体化仿真模型.仿真结果表明,针对滚仰式捷联导引头设计的基于BP神经网络的PID控制器的动态性能优于传统PID控制器,由此建立的导引头稳定与跟踪一体化仿真模型可以实现对导引头的闭环控制,能够对机动目标实现快速稳定的跟踪,在实际工程应用中为滚仰式捷联导引头的控制提供有益的参考.     

红外导弹目标模系统的实现

红外导弹采用探测目标红外热源实现对目标的追踪,追踪过程中利用红外导引头自身的目标识别功能排除环境干扰和人工干扰.为完成导弹的测试,建立了一套红外目标模拟系统,制定了详细的定性和定量校准方案,实现了红外目标和人工诱饵干扰的动态模拟.系统具有精度高、覆盖性广、通用性强和全程自动化测试的特点,为控制舱的性能研究、设计改进和生产交付提供了一个良好的目标模拟平台.     

一种结构模式可变的组合导航算法研究

根据毫米波/红外双导引头数据融合装置在工程应用中对性能指标的特殊要求,提出了一种新的组合导航算法。该算法根据各传感器的工作状态,自适应改变跟踪算法的结构模式,从而有效缩短目标截获时间、增长截获距离、提高跟踪精度。通过计算机仿真,验证了该算法的可行性。该算法也可以推广到其他异类传感器构成的组合导航系统中去。     

基于正交试验的运动目标跟踪算法性能评价

针对目前运动目标跟踪算法性能评价中测试数据量大、试验次数多以及未充分考虑多因素组合场景下的算法性能表现等问题,提出一种基于正交试验的运动目标跟踪算法性能评价方法。分析影响算法性能的因素和水平,构建正交试验数据集,通过该数据集测试算法性能并利用极差分析法分析数据结果,以得到各影响因素间的强弱关系以及算法性能表现较好时的因素水平组合方式。分析结果表明,该方法能够全面、有效地评估运动目标跟踪算法的性能,减少测试次数和数据量,并为其他图像处理算法的性能评估提供参考。     

A new tracker for air-to-air missile targets

An extended Kalman filter is developed to aid the tracking of an air-to-air missile from a maneuvering target aircraft. The filter exploits knowledge of the dominant aerodynamically induced lift and drag forces of a nonthrusting missile employing proportional navigation guidance, and it also accounts for the dynamic lag and bandwidth effects of the missile seeker, guidance, and control systems. Incorporating the refined missile acceleration model enhances the filter's tracking estimate precision and provides meaningful threat predictive capabilities. Identifiability of parameters within the acceleration model is established, an adaptive filter is developed, and its performance capabilities portrayed through realistic Monte Carlo simulations.     

基于光照自适应动态一致性的无人机目标跟踪

无人机跟踪任务经常面临各种光线变化场景,然而无人机跟踪方法主要在光线充足下实现鲁棒跟踪。提出一种具有光照自适应性和跨帧语义感知动态一致性评估的无人机跟踪方法,实现光线不足下的无人机目标跟踪。首先构建光照自适应模块对昏暗场景进行识别,对视频图像的光照强度进行补偿;其次构建目标模板训练具有目标感知能力的滤波器进行相关运算,并利用跨帧之间的响应信息进行一致性评估;最后构建动态约束策略并对响应差异进行约束,使跟踪器保持时间平滑。在UAVDark135和UAV123数据集上,与9种先进算法进行对比实验,结果表明该算法具有较好的跟踪性能。     

基于孪生网络融合多模板的目标跟踪算法

基于全卷积孪生网络的视频目标跟踪算法由于在跟踪过程中使用单一模板,在运动目标外观发生变化时容易出现跟踪漂移并导致精度下降.因此,提出了一种基于孪生网络融合多模板的目标跟踪算法.该算法可在特征级上建立模板库,并使用平均峰值相关能量和模板相似度来保证模板库中各个模板的有效性,从而对多个响应图进行融合以获得更高的跟踪精度.O...     

Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.     

Filling the gap in quality assessment of video object tracking

Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms.     

运算资源受限环境下的目标跟踪算法综述

自深度神经网络出现以来，目标跟踪技术领域的发展也取得了长足的进步。当前目标跟踪领域的研究大多数都集中在算力充沛的计算环境下提升算法的准确度与效率，在运算资源受限环境下的目标跟踪算法研究相对较少。因此，开发在运算资源受限环境下仍然有效的跟踪网络至关重要。对近年目标跟踪技术所取得的进展与设计理念进行了系统性的梳理，并总结其在适配运算资源受限环境下的改进。介绍了目标跟踪任务的整体工作流程，并根据各跟踪方法的侧重点做出归纳。总结了目标跟踪任务中已有的数据集与可用于模型评估的评价指标，以便于研究人员根据实际任务的需求来确定具体使用的数据集；结合现有的工作，发掘未来的研究方向。     

Probabilistic data fusion on a large document collection

Data fusion is the process of combining the output of a number of Information Retrieval (IR) algorithms into a single result set, to achieve greater retrieval performance. ProbFuse is a data fusion algorithm that uses the history of the underlying IR algorithms to estimate the probability that subsequent result sets include relevant documents in particular positions. It has been shown to out-perform CombMNZ, the standard data fusion algorithm against which to compare performance, in a number of previous experiments. This paper builds upon this previous work and applies probFuse to the much larger Web Track document collection from the 2004 Text REtreival Conference. The performance of probFuse is compared against that of CombMNZ using a number of evaluation measures and is shown to achieve substantial performance improvements.     

Invisible Marker–Based Augmented Reality

To estimate camera pose, existing augmented reality (AR) techniques usually require fiducial markers with known geometry. However, placing a marker in the workspace of the user can be very visually intrusive. To overcome this limitation, a nonintrusive AR method using invisible markers that are created/drawn with an infrared (IR) ink is proposed and an IR marker tracking system is presented. This system includes additional algorithms to maintain reliable performance in a cluttered background. Then, the working conditions of IR markers are examined and compared with those of visual markers, and the optimal working conditions of IR markers are discussed. Next, the qualitative evaluation of an IR marker–based AR is presented through a variety of experiments and user evaluations. Finally, the potential applications of IR marker–based AR are explored.     

Benchmarking template-based tracking algorithms

For natural interaction with augmented reality (AR) applications, good tracking technology is key. But unlike dense stereo, optical flow or multi-view stereo, template-based tracking which is most commonly used for AR applications lacks benchmark datasets allowing a fair comparison between state-of-the-art algorithms. Until now, in order to evaluate objectively and quantitatively the performance and the robustness of template-based tracking algorithms, mainly synthetically generated image sequences were used. The evaluation is therefore often intrinsically biased. In this paper, we describe the process we carried out to perform the acquisition of real-scene image sequences with very precise and accurate ground truth poses using an industrial camera rigidly mounted on the end effector of a high-precision robotic measurement arm. For the acquisition, we considered most of the critical parameters that influence the tracking results such as: the texture richness and the texture repeatability of the objects to be tracked, the camera motion and speed, and the changes of the object scale in the images and variations of the lighting conditions over time. We designed an evaluation scheme for object detection and interframe tracking algorithms suited for AR and other computer vision applications and used the image sequences to apply this scheme to several state-of-the-art algorithms. The image sequences are freely available for testing, submitting and evaluating new template-based tracking algorithms, i.e. algorithms that detect or track a planar object in an image sequence given only one image of the object (called the template).     

智能视频算法评估综述

智能视频监控在公共安全和民用领域已经显现出有效与广阔的应用前景,备受国内外学者和众多实际工程领域专家的高度关注,制定有效、公正的智能视频算法评估准则是其应用过程中的一个重要环节.文中对当前国际上智能视频算法的评估工作进行了比较全面的综述,重点是运动检测、目标跟踪的性能评估.最后指出了智能视频算法评估的发展方向.     

ARMA模型在光纤陀螺惯导系统寻北中的应用

在光纤陀螺捷联惯导系统寻北过程中,光纤陀螺的随机漂移是影响其精度的重要因素。论文提出了一种建立三阶自回归模型AR（3）方法,实现了高精度FOG捷联惯导系统静态输出信号的在线建模,并采用强跟踪卡尔曼滤波技术进行误差的实时滤波。寻北试验结果表明,寻北精度有了明显的提高,从而验证了方法的可行性,具有工程实用性。     

基于图像中心矩的快速模板匹配算法

提出了一种以中心矩作为匹配特征，基于积分图像的思想，并采用粗、精阶段相结合的相关跟踪算法。实验结果表明，与以往的以象素作为图像特征的相关跟踪算法不同，本算法运算速度快、实时性强、匹配精度高。采用Visual C 编程，可在40ms，即一个电视周期内完成对图像的处理。本算法在运动目标跟踪、模式识别及机器视觉等领域有着广泛的应用前景。     

Performance Characterization of Image Stabilization Algorithms

This paper compares three image stabilization algorithms when used as preprocessors for a target tracking application. These algorithms vary in computational complexity, accuracy, and ability. Algorithm 1 is capable of only pixel-level realignment of imagery, while Algorithms 2 and 3 are capable of full subpixel stabilization with respect to translation, rotation, and scale. The algorithms are evaluated on their performance in the stabilization of one synthetic forward looking infrared (FLIR) data set and two real FLIR imagery data sets. The evaluation tools incorporated include mean absolute error of the output data set and the overall performance of an automatic target acquisition system (developed at the Army Research Laboratory) that uses the algorithms as a front end preprocessor. We found that for this tracking application, extremely accurate subpixel stabilization was a requirement for proper operation. We also found that in this application, Algorithm 3 performed significantly better than the other two algorithms.