Robust data fusion for multisensor detection systems

Minimax robust data fusion schemes for multisensor detection systems with discrete-time observations characterized by statistical uncertainty are developed and analyzed. Block, sequential, and serial fusion rules are considered. The performance measures used, and made robust with respect to the uncertainties, include the error probabilities of the hypothesis testing problem in the block fusion case and the error probabilities and expected numbers of samples or sensors in the sequential and serial fusion cases. For different sensor observation statistics, the minimax robust fusion rules are derived for two asymptotic cases of interest: when the number of sensors is large and when the number of times the fusion center collects the local (sensor) decisions is large. Moreover, for the case of identical sensor observation statistics and a large number of sensors, it is shown that there is no loss in optimality, if local tests using likelihood ratios and equal thresholds are used in the sequential fusion rule. In all situations, the robust decision rules at the sensors and the fusion center are shown to make use of likelihood ratios and thresholds that depend on the least-favorable probability distributions of the uncertainty class describing the statistics of sensor observations     

An introduction to multisensor data fusion

Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion     

Tutorial 14: multisensor data fusion

Multisensor data fusion is an emerging discipline. The rapid evolution of high-performance, inexpensive, and low-power computing components for pervasive systems, such as wireless sensor networks, will enable the development of complex sensor fusion applications. The idea underlying data fusion is to combine information collected by different sensors, to make inferences at different levels of abstraction about an entity or a situation, and to enable either prompt human reactions or autonomous machine decisions. This paper presents the basic overview of data fusion terminology, models, and algorithms with the help of some examples related to next- generation car safety and driver assistance systems.     

光电多传感器信息熔合系统

本文从系统观点阐述了多传感器信息熔合系统的一般概念、组成、体系结构、实现技术和应用范例,指出这是光电对抗、现代作战中必不可少的系统。     

一种多传感器数据融合仿真平台的设计

数据融合是现代C^3I系统的重要组成部分,对C^3I系统效能的发挥起着决定性的作用。本文针对第一级目标运动学数据融合,构建了通用的可视化多传感器数据融合仿真平台,为研究者提供了进行各种融合算法仿真验证和定量评估的仿真环境。进行了合理的软件设计,模块化和开放性的结构使仿真平台的通用性得到了体现,与通信链路连接即可处理真实的目标数据。最后通过在本系统运行的一个软件实例,证明了仿真平台的有效性与实用性。     

多传感器图像融合综述

图像融合是在多测度空间综合处理多源图像和图像序列的技术。图像融合分为：像素层图像融合、特征层图像融合和符号图像层融合。本文讨论了图像融合各层次的融合算法以及红外图像、可见光图像、多谱图像、雷达图像等的融合问题，提出图像融合的结构模型，并对图像融合的发展方向进行了展望。     

一种多传感器空情信息融合算法

针对防空指挥系统快速反应决策能力要求,给出了可行的系统信息融合处理软件组成结构及功能描述,提出了一种适用于快速空情信息融合的通用信息融合处理方法和模型算法。建模过程中,提出的信息融合处理方法和模型算法将防空指挥系统信息融合的复杂过程条理化、公式化和数字化。仿真结果证明,该方法是正确和有效的。目前,该方法已应用到实际工程中。     

A data fusion architecture for enhanced position estimation inwireless networks

The problem of position determination of a mobile user in a wireless network has been studied extensively-particularly in the context of military operations and emergency 911 service in cellular networks. The Global Positioning System (GPS) and network-based angle of arrival (AOA), time difference of arrival (TDOA), and time of arrival (TOA) techniques offer solutions to the position estimation problem. However, each method has limitations that prevent its adoption as a universal solution. Data fusion is a collection of methods and techniques for merging disparate types of information. We present a data fusion model for the enhanced accuracy of position estimates within wireless networks. We illustrate our model with several examples and conclude with recommendations for future research directions     

Optimal dimensionality reduction of sensor data in multisensor estimation fusion

When there exists the limitation of communication bandwidth between sensors and a fusion center, one needs to optimally precompress sensor outputs-sensor observations or estimates before the sensors' transmission in order to obtain a constrained optimal estimation at the fusion center in terms of the linear minimum error variance criterion, or when an allowed performance loss constraint exists, one needs to design the minimum dimension of sensor data. This paper will answer the above questions by using the matrix decomposition, pseudo-inverse, and eigenvalue techniques.     

大数据架构剖析及数据安全融合技术

从剖析大数据通用技术架构入手,总结数据处理的相关流程;分析和对比数据中台、分级构建两种模式的业务场景、管理架构、建设难度、技术要求、数据处理等,总结其适用范围和优缺点;最后聚焦多级架构模式下数据安全融合技术问题,提出相应的解决办法,为行业大数据构建选型提供有益的借鉴和参考。     

A novel region feature used in multisensor image fusion

A new region feature which emphasized the salience of target region and its neighbors is proposed. In region segmentation-based multisensor image fusion scheme, the presented feature can be extracted from each segmented region to determine the fusion weight. Experimental results demonstrate that the proposed feature has extensive application scope and it provides much more information for each region. It can not only be used in image fusion but also be used in other image processing applications.     

Theoretical analysis of improvement of track loss in clutter with multisensor data fusion

The paper analyses the improvement of track loss in clutter with multisensor data fusion.By a determination of the transition probability density function for the fusion prediction error, one can study the mechanism of track loss analytically. With nearest-neighbor association algorithm. The paper we studies the fused tracking performance parameters, such as mean time to lose fused track and the cumulative probability of lost fused track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fused tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.     

基于LMMSE估计的多传感器目标航迹融合

介绍一种基于线性最小均方差（LMMSE）估计的多传感器目标航迹融合方法。这种方法适于分布式多传感器目标跟踪系统，它具有LMMSE估计器的许多优点。同时，由于它考虑了因公共的目标动态过程噪声引起的估计误差的非独立性的影响，因此，它比基于独立性假设的方法更精确。     

ELSA: a multisensor integration architecture for industrial grading tasks

This paper presents the topology of the Extended Logical Sensor Architecture (ELSA) for multisensor integration and the methodology for constructing industrial sensor integration systems based on this architecture. ELSA has been developed for industrial applications, particularly, the on-line grading and classification of non-uniform food products. It addresses a number of issues specific to industrial inspection. The system must be modular and scalable to accommodate new processes and changing customer demands. It must be easy to understand so that non-expert users can construct, modify, and maintain the system. Furthermore, a data representation scheme which allows for the quantification of product deviations from an ideal model is required.To address these needs, the sensors are encapsulated by a logical sensor model, providing robustness and flexibility. The construction methodology is based upon the object model which represents object classifications through combinations of primary features weighted by fuzzy membership functions. The features guide the selection of sensors and processing routines; the classifications determine the rulebase used by the inference engine for process decisions. Although inspection is the focus of this work, it is intended to be applicable to a variety of automation tasks which may benefit from a multiple sensor perception system.     

基于多源数据融合技术的情报侦察系统效能评估体系

多源数据融合技术因具有多类数据源,可多层次、全方位感知信息,已被广泛应用于军事领域.但目前缺少一个统一的评价体系,用于指导和规范多源数据融合情报侦察系统的效能评估.为了全面评价和比对多源信息融合情报侦察系统各方面的性能,在分析相关理论的基础上,建立了一个合理的且相对全面的评价指标体系,覆盖了多源数据融合情报侦察系统各方面的属性,并分析了评价体系与指标体系之间的关系.该项研究丰富了多源数据融合效能评估理论和方法,可用于指导和规范多源数据融合情报侦察系统的效能评估工作,并有效地提高了系统的综合作战能力.     

一种多传感器图像融合与数字水印技术

在小波包域提出了一种多传感器图像融合和双水印算法。首先,利用HIS变换和小波包变换将多光谱和全色图像分解为多个高低频子带,根据小波包域系数特点,低频部分采用基于区域平均能量加权算法的规则进行融合,高频部分采用绝对值取大的规则进行融合。然后,在高低频图像融合系数分别嵌入一个水印,低频水印利用了离散余弦变换的聚能去相关能力,高频水印利用了图像纹理子块特征。最后,对嵌入的双水印融合图像进行攻击和分析。实验结果显示,融合图像在保留多光谱图像光谱信息的基础上有效提高了空间分辨率;加水印的融合图像具有良好的不可视性和鲁棒性。     

一种像素级多传感器图像融合算法的研究

该文给出了一种像素级多传感器图像融合算法,并将其应用于不同波段SAR图像的融合。该算法先用基于卡尔曼滤波的图像融合技术得到一幅灰度融合图像,再用不同的RGB颜色通道分别表示灰度融合图像以及原图像中增强后的细节,最终得到一幅彩色融合图像。仿真结果表明,用此算法得到的彩色融合图像包含更多的细节,可辨识性强。     

基于LMI方法的参数不确定多传感器系统的集中式融合估计

研究了范数有界不确定描述模型和多胞形不确定描述模型所代表的一类参数不确定多传感器系统的集中融合估计问题。应用H∞理论详细地比较了基于不同模型的两种融合方法时域和频域功能，给出了集中鲁棒融合估计的理论结果。并应用MATLAB的LMI工具箱，分析了仿真计算结果。     

数据链网络信息融合的有中心无中心处理结构

针对数据链网络的发展趋势及应用需求,揭示了有中心无中心融合处理的基本原理,并对其处理流程与关键技术进行了深入分析,并提供了解决思路及仿真分析。在此基础上,结合机载信息融合的功能及应用,分析对比了有中心无中心融合处理结构的优缺点,并提出了适合当前数据链网络现状的融合处理方式建议,对网络化多平台信息系统的信息融合架构设计、方案选择具有重要参考意义。     

基于多自治代理的一种分布式入侵检测系统

提出了一种基于多自治代理的分布式入侵检测系统结构，该结构不仅能够避免分布式系统中存在的单点失效问题、平衡计算并提高系统整体的效率，还可以大大的降低系统对网络入侵行为的响应时间，使入侵检测系统能够适应计算机及互联网的迅速发展。