基于多传感器信息融合技术的智能视频监控系统研究

介绍了一种基于多传感器信息融合技术的智能视频监控系统的信息融合设计.为了降低监控系统中的单一传感器的误报率和漏报率,通过对视频监控系统的人体红外、视频摄像等多种传感器的网络集成,将不同传感器采集的不同的描述信息进行了有效地特征提取和传输,并运用模糊神经网络的多传感器信息融合算法进行多次仿真实验.实现了该系统对检测区域的...     

基于VxSim的信号融合设计与模拟仿真

在设计信号接收机时,针对实践运用过程中出现杂散电磁信号后影响用户实际使用的情况,提出了一种信号融合设计实现方法。方法使用VxSim模拟仿真信号融合过程,通过模拟多个实时任务,模拟生成信号,对融合池中信号的权重计算,同时对比三种融合计算方式,选择线性权重计算和非线性权重计算的叠加方法,输出最大权重的融合结果。有效解决了单一信号的不稳定性,保证了信号接收机的可靠性,同时为嵌入式实时多任务软件开发提供了设计环境和思路。     

图像融合技术在成像中的应用

阐述了基于小波变换的图像融合的基本原理、方法及优点.通过对可见光图像与红外图像进行融合的实验比较表明,以图像的边缘特征为融合准则的融合效果更好.这种方法比较适用于场景监控的应用领域.它突出了目标的边缘,从而使观察者更容易发现和识别目标.     

国外信息融合技术概述

随着传感器技术的不断发展和战争的需求，多传感器得以大量使用。使用多传感器将导致信息量大增。人工综合处理这些信息的方式已不适应现代战争的要求，只能采用自动综合与处理的方式，这就是本文所指的信息融合。本文主要介绍信息融合的应用范围，主要研究内容，信息处理技术、信息融合方法和信息融合系统结构。     

遥感图像融合效果评估方法

遥感图像融合效果评估是目前遥感图像融合领域中亟待解决的问题,直接影响遥感图像融合处理技术的发展.文中在构建多源遥感图像融合处理估计模型的基础上,重点讨论分析遥感图像融合效果的主、客观评价技术的发展现状,进行SAR图像与光学图像的融合处理仿真实验,优选相关系数及峰值信噪比完成SAR图像与光学图像的融合效果评估处理.同时,还讨论了目前图像融合效果评估中存在的主要问题,对进一步研究图像融合效果评估有很好的意义.     

基于融合特征降维的场景分类研究

场景分类中使用了许多种类的图像特征,但通常情况下,一种特征很难对许多不同场景都得到不错的分类结果,故而对特征融合方面做了很多研究工作.但特征融合的方法存在一个问题,即一般维数会很高,这个高维的特征向量可能包含冗余信息和噪声,从而降低最终的分类准确率.因此提出了使用PCA对融合的特征进行降维以去除冗余信息和噪声,经实验验证,该方法提高了分类的准确率.     

基于神经网络信息融合隧道CO浓度预测研究

高速公路隧道监控系统存在传感器单一、可靠性低等问题,如果能对失效传感器数据预测,可提高隧道安全性。利用BP和RBF神经网络的非线性逼近能力融合提取隧道CO浓度信息特征,再用最优均方误差加权融合算法对两种网络分别提取的信息再次融合预测隧道CO浓度。提高了模型预测精度,进而给出数据预处理方法和模型评价指标。仿真实验表明:该模型对隧道CO浓度预测的有效性,性能优于单一神经网络融合预测模型。     

浅谈地区电网调控融合的实施与风险控制

将变电设备集中监控业务与电网调度业务融合,完成调控融合,是深化"大运行"体系建设的必然选择.为做好地区电网调控融合工作,本文提出了一系列措施,大力推进调控融合落地实施,并提出必要的预控措施,降低融合过程中存在的风险,确保调控融合的安全平稳过渡.     

多传感器信息融合技术及其应用

随着科学技术的迅猛发展,信息采集与融合趋于多元化和复杂化.单一的传感器已经无法满足需求.因此,多传感器信息融合技术开始广泛应用于各行各业,并提高了信息处理的准确性和完整性.从概念、结构与分类、关键算法的研究和实际应用等方面对多传感器信息融合技术进行了介绍,并总结了该技术的发展趋势与未来前景.     

多传感器信息融合研究综述

多传感器信息融合是实现无人驾驶的核心技术,多个传感器之间协同收集车辆周围环境的数据信息,经过多传感器融合结构的转换和处理,使用融合算法进行联合分析,能够使车辆全面地感知驾驶环境,帮助车辆完成自主导航、变道、控制速度等智能决策。基于多传感器信息融合的基本定义,从功能模型和结构模型介绍多传感器信息融合的基本形式;重点梳理多传感器信息融合的算法,分为随机类和人工智能类两个大类,详细分析各方法的原理及特点;最后总结出多传感器信息融合策略在实际应用时的主要步骤,同时分析其在无人驾驶场景中的应用,为多传感器信息融合未来理论研究方向和应用实践方向提供参考,从而完成多传感器信息融合的综合分析。     

多源信息融合中的位置级融合评述

简要介绍了信息融合理论中的位置级融合的理论及相关算法，并分析了鲁棒融合的必要性、状态与噪声的相关性、研究非标准非线性系统融合算法的重要性及信息融合在工业过程控制系统中的应用。     

基于分级信息融合的引信多模融合

为了提高引信的准确性,结合当前微型毫米波雷达、激光传感器、微机电系统( MEMS)传感器对目标的探测能力,提出了一种引信多模融合策略。以传感器受环境影响的主要因素为输入,构建环境影响贝叶斯网络,形成传感器可靠性权重分配;通过建立传感器量测加权融合,形成测距与加速度两级融合综合处理获得目标准确位置信息,从而提高量测可靠性,为准确引爆提供支撑。实验表明该算法可提高测距准确性且对不同环境具有较好的适应性。     

Optimal linear estimation fusion .I. Unified fusion rules

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.     

Nuclear fusion advances

The last decade has seen advances in the shaping and confinement of plasmas, and in approaches to noninductive current drive. Here, the author presents an overview of nuclear fusion advances between 1983-93 examining: fusion milestones; plasma shaping; bootstrap current; tokamaks; noninductive heating; current drives; ITER; and impurities control     

Multisensor data fusion

Multisensor data fusion refers to the acquisition, processing and synergistic combination of information gathered by various knowledge sources and sensors to provide a better understanding of a phenomenon. It is a fascinating and rapidly evolving field that has generated a lot of excitement in the research and development community. These concepts are being applied to a wide variety of fields such as military command and control, robotics, image processing, air traffic control, medical diagnostics, pattern recognition and environmental monitoring. This paper presents a brief overview of the field and illustrates its potential by means of two examples     

基于信息融合的高校招生录取数据融合系统

高校招生录取数据是高校工作重要原始资料。利用信息融合概念、VF软件技术、Excel软件技术设计高校招生录取数据融合系统,可以帮助招生工作人员减少录取考生信息统计工作量,提高统计分析速度和准确度,及时统计分析录取数据,为领导决策以及高校各部门相关工作提供可靠数据信息,为提高招生工作信息化水平做出了较好的尝试。     

Adaptive region-based image fusion using energy evaluation model for fusion decision

A new adaptive region-based image fusion approach is proposed. To implement image segmentation, the piecewise smooth Mumford-Shah segmentation algorithm is studied and a fast and simple method is proposed to solve the energy function. Two complementary functions u ⁺ and u ⁻ of the algorithm, which are respectively looked as objects and background of the image, are extended into the whole image domain, and they are computed by linear or nonlinear diffusion. The key to the algorithm is to make optimal fusion decisions for every segmented region. For this purpose, an evaluation approach has to be given to measure the performances of the available fusion rules. Therefore an energy-based evaluation model, derived from the Total Variation principle, is proposed. By numerical experiment it has been demonstrated that despite an increase in complexity, the new approach has shown a number of advantages over previous ones, for example the ability to preserve all relevant information and remove some of side effects such as reducing contrast and sensitive to error of registration.     

Novel cooperative neural fusion algorithms for image restoration and image fusion.

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.