An integrated approach to high-level information fusion

In today’s fast paced military operational environment, vast amounts of information must be sorted out and fused not only to allow commanders to make situation assessments, but also to support the generation of hypotheses about enemy force disposition and enemy intent. Current information fusion technology has the following two limitations. First, current approaches do not consider the battlefield context as a first class entity. In contrast, we consider situational context in terms of terrain analysis and inference. Second, there are no integrated and implemented models of the high-level fusion process. This paper describes the HiLIFE (High-Level Information Fusion Environment) computational framework for seamless integration of high levels of fusion (levels 2, 3 and 4). The crucial components of HiLIFE that we present in this paper are: (1) multi-sensor fusion algorithms and their performance results that operate in heterogeneous sensor networks to determine not only single targets but also force aggregates, (2) computational approaches for terrain-based analysis and inference that automatically combine low-level terrain features (such as forested areas, rivers, etc.) and additional information, such as weather, and transforms them into high-level militarily relevant abstractions, such as NO-GO, SLOW-GO areas, avenues of approach, and engagement areas, (3) a model for inferring adversary intent by mapping sensor readings of opponent forces to possible opponent goals and actions, and (4) sensor management for positioning intelligence collection assets for further data acquisition. The HiLIFE framework closes the loop on information fusion by specifying how the different components can computationally work together in a coherent system. Furthermore, the framework is inspired by a military process, the Intelligence Preparation of the Battlefield, that grounds the framework in practice. HiLIFE is integrated with a distributed military simulation system, OTBSAF, and the RETSINA multi-agent infrastructure to provide agile and sophisticated reasoning. In addition, the paper presents validation results of the automated terrain analysis that were obtained through experiments using military intelligence Subject Matter Experts (SMEs).     

基于证据理论的群组DEMATEL改进方法

在群组DEMATEL（决策试验与评价实验室）分析过程中,科学合理地集成群组专家意见是保证群决策有效性的技术核心。然而现有文献不仅没有对群组专家意见的集成机理作出科学合理的解释,而且还存在单专家对因素关系判断过于主观武断的问题。为克服上述缺陷,在概述传统群组DEMATEL及系统分析其缺陷的基础上,提出了一种基于证据理论的群组DEMATEL改进方法。该方法优点在于：一方面以信度函数反映决策信息的不完备性,并通过Dempster组合规则有效集成群组专家意见;另一方面以整体判断思想实现群组专家证据信息的交互。实例验证结果表明,改进方法是科学可行的,有着较强的实际应用可操作性。     

An integrated linguistic-based group decision-making approach for quality function deployment

Quality function deployment (QFD) is a well-known customer-driven approach for new or improved product/service design and development to maximize customer satisfaction. A typical QFD analysis process involves a series of group decision-making (GDM) processes, such as determination of the importance of customer requirements (CRs), the relationship between CRs and engineering characteristics (ECs), and the correlation among ECs. Properly handling these GDM processes is essential because it will significantly affect the prioritization of ECs, the target value setting of ECs, and the following deployment phases of QFD. Due to different personal experiences and/or lack of sufficient knowledge and information, decision-makers who participate in the QFD analysis process tend to provide their opinions by using different types and multi-granularity linguistic information, which are inherently vague and imprecise. Unlike most of the previous studies, which excessively rely on fuzzy approaches, this study proposes an integrated linguistic-based GDM approach, which can compute with words directly and avoid the risk of loss of information, to cope with multiple types and multi-granularity linguistic assessments given by a group of decision-makers in QFD activity process. Finally, a numerical example is taken to illustrate the applicability of the proposed approach. The linguistic-based approach can effectively manage the imprecise and vague input information in QFD and facilitate decision-making in product design and development.     

基于前景理论的风险型混合模糊多准则群决策

针对准则值为模糊数的风险型多准则群决策问题,提出一种基于前景理论的多准则群决策方法.首先运用方差分析原理构建群决策参考点;然后分析区间数、三角模糊数、梯形模糊数等无量纲化方法,给出各类模糊数的价值函数计算方法,并提出群体信息集成决策步骤;最后通过算例表明所提出方法的有效性和可行性.     

基于证据理论的偏好型直觉模糊群决策方法

针对直觉模糊多属性群决策问题,研究属性和专家权重的确定以及信息的集结方法.利用直觉模糊熵确定属性客观权重,并根据偏好信息确定合理的属性综合权重;在属性层面区分专家权重,将直觉模糊评价值作为Mass函数,构建证据冲突度模型确定专家权重,并利用犹豫度加以修正,避免综合支持度低而对方案排序影响大的专家权重过分削弱;采用证据理论集结决策信息,根据得分值进行方案排序.最后通过算例分析,验证了所提出方法的合理性和有效性.     

基于证据理论的信息融合系统设计与实现

为了对多传感器获取的不完整信息进行融合,提出了基于证据理论的信息融合方法,并将神经网络技术引入到证据合成规则中,建立了新的合成规则。在此基础上,采用面向对象的系统设计开发方法,分析了系统的体系结构,建立了各功能模块,并利用Visual C++ 6.0开发了基于证据理论的信息融合系统。     

基于D-S证据理论和BP神经网络的多传感器信息融合

针对多传感器信息融合的基本可信度分配在实际应用中难以解决的问题,提出了一种基于D-S证据理论与BP网络相结合的多传感器信息融合的改进方法。该方法充分发挥BP神经网络自学习、自适应和容错的能力,利用BP神经网络处理证据理论的基本可信度问题,再利用D-S证据理论来处理不精确、模糊的信息。最后通过一个实例证明了该方法的有效性。     

基于改进D-S证据理论的室内环境控制决策系统

针对室内环境因子多且相互作用关系复杂,影响室内环境舒适度的控制精准决策,设计了一种基于改进D-S证据理论的室内环境控制决策系统。首先采用箱线图法和均值替代法检测修复异常采集数据,然后利用距离自适应加权融合算法实现同类传感器数据一级融合,最后利用改进D-S证据理论算法,实现全局融合决策。实验结果表明,改进D-S证据理论算法能够有效避免冲突证据带来的融合决策误差,系统可以实现室内环境控制的精准决策,融合决策精度高,具有一定的推广应用价值。     

基于粗糙集和D-S证据理论的信息融合技术应用研究

针对井下信息量大、噪声多、参数多、动态等特征,提出了一种基于粗糙集数据挖掘和D-S证据理论优化信息融合技术的矿井环境监测方法。采用粗糙集对井下信息进行预处理;利用径向基函数(RBF)神经网络建立了井下环境识别模型;利用D-S证据理论进行两级融合决策,并对井下安全状况进行判断。仿真结果表明:该方法提高了井下信息的识别和决策效果,极大地降低了不确定性。     

An improved grey group decision-making approach

In complex group decision-making, decision makers and decision attributes are the core of the relevant activities. Targeting the problem of scheme ranking and behavioural characteristics that exist in group decision-making, from the perspective of group negotiation and decision-making system coordination, by exploiting the grey target and grey relation analysis, this paper establishes a novel grey group decision-making approach. We define a group measure matrix of scheme, consensus ideal scheme, and decision-making resource coefficient. Then, by borrowing Nash’s bargaining idea, and maximizing group negotiation satisfaction and minimizing system coordination deviation, we construct a two-step optimization model to solve for the group consensus ideal scheme and its measure value matrix. In addition, we take decision-making schemes as research objects; and from the two dimensions of decision maker and attribute, we characterize and measure the closeness degree of decision maker information and attribute information by using the distance between the group measure matrices of scheme and consensus ideal scheme, so that we are able to construct a novel grey scheme matrix similar incidence analysis model. Lastly, we take the group decision-making problem of selecting the location of a garbage disposal station as a case analysis, and explore the economic significance and theoretical value of the model.     

基于后悔理论和证据理论的犹豫模糊决策方法

针对属性值为犹豫模糊信息、属性权重和自然状态发生概率完全未知的多属性群决策问题,考虑决策者心理行为,提出一种基于后悔理论和证据理论的多属性群决策方法。首先,运用证据理论计算各自然状态发生的概率;然后,基于区间模糊矩阵、t-分布估计以及得分函数矩阵确定属性信息的效用值,进而依据后悔理论得到每个自然状态下的感知效用矩阵;通过加权算术平均得到综合感知效用矩阵,并依据方案综合感知效用的大小确定方案优劣排序;最后,将所提方法运用于对投资公司的选择实例中。实验结果表明,虽然所提方法与现有方法得到的决策结果相同,但是所提方法在决策过程中只需考虑较少数量的参数。对比分析实验表明,所提方法得到的决策结果合理、可靠,且能反映实际决策情况。     

一种多准则纯语言群决策方法

针对权系数信息和方案的准则值为确定语言等级,或位于两个语言等级之间,甚至缺失的群决策问题,提出一种新的决策方法.该方法利用证据推理算法对准则权系数和方案值在准则下进行群体集成,采用二元语义对方案进行语言集结,并用方案与理想方案的二元语义间距离和群体集成信息等构建非线性规划模型,使用遗传算法求解优化模型,进而得到方案的排序.最后通过实例说明该方法的可行性和有效性.     

基于改进贝叶斯粗糙集和证据理论的决策信息融合方法

针对决策信息信息表中新对象的分类问题,提出一种基于改进的贝叶斯粗糙集和证据理论的决策信息融合方法.对传统的贝叶斯粗糙集进行改进,扩展到多决策类,定义了支持度的概念以此反映确切分类的对象所占的百分比.利用贝叶斯粗糙集的支持度和置信增益函数作为证据的支持程度,得到各准则下的证据基本概率分配函数,并利用证据合成法则对多个证据进行合成,以此进行决策.将上述方法应用于设备故障的诊断问题中,通过方法的对比验证了该方法实践应用的有效性.     

基于模糊粗糙集和D-S证据理论的多源灌溉信息融合方法

针对多源灌溉信息决策过程中不确定性信息难以融合的问题,提出了一种基于模糊粗糙集和D-S证据理论相结合的决策融合方法。运用模糊粗糙集理论,建立基本概率分配函数,计算各灌溉因子与灌溉决策之间的依赖程度,构建多个融合灌溉因子对灌溉决策的识别框架;然后运用改进的D-S证据理论,进行多源灌溉信息决策层级的融合,最终解决不确定信息的表达和合成问题。应用上述方法对华北地区冬小麦土壤水分、光合速率和气孔导度等信息进行灌溉决策融合,结果显示：灌溉决策的不确定性由融合前的最高38%降至9.84%,该方法可有效地提高灌溉决策精度,降低灌溉决策的不确定性     

Large group decision-making based on interval rough integrated cloud model

Since different uncertainties exist in the large group decision-making (LGDM) process, such as randomness, diversity and fuzziness, a single method may be insufficient to address LGDM. Hence, this paper proposes a hybrid model that a new similarity calculation method for cloud model, the netting clustering and interval rough integrated cloud (IRIC) are combined to solve LGDM in uncertain linguistic environment. First, a new similarity method for cloud model is presented, based on which a netting clustering method is provided. The similarity calculation method has higher differentiation degree and has overcome some shortcomings of previous ones. Second, two hybrid-weighting methods are utilized respectively to calculate expert weights and attribute weights for making the decision-making process more credible and scientific. Finally, the IRIC method is applied to LGDM for dealing with the randomness and uncertainty. In addition, an example is offered to demonstrate the application of the proposed approach. According to a time-consuming test, the proposed method is more suitable to address LGDM in the big data environment.     

Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory

Engine diagnostics is a typical multi-sensor fusion problem. It involves the use of multi-sensor information such as vibration, sound, pressure and temperature, to detect and identify engine faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, multi-sensor based engine diagnosis can be viewed as a problem of evidence fusion. In this paper we investigate the use of Dempster–Shafer evidence theory as a tool for modeling and fusing multi-sensory pieces of evidence pertinent to engine quality. We present a preliminary review of Evidence Theory and explain how the multi-sensor engine diagnosis problem can be framed in the context of this theory, in terms of faults frame of discernment, mass functions and the rule for combining pieces of evidence. We introduce two new methods for enhancing the effectiveness of mass functions in modeling and combining pieces of evidence. Furthermore, we propose a rule for making rational decisions with respect to engine quality, and present a criterion to evaluate the performance of the proposed information fusion system. Finally, we report a case study to demonstrate the efficacy of this system in dealing with imprecise information cues and conflicts that may arise among the sensors.     

Face recognition based on a group decision-making combination approach

This paper proposes a novel and real-time classifiers combination approach, group decision-making combination (GDC) approach, which can dynamically select the classifiers and perform linear combination. We also prove that the orthogonal wavelet transform can be regarded as an effective image's preprocessing tool adapted to classifiers combination. GDC has been successfully used for face recognition, which can improve on the recognition rate for the algebraic features. Experiment results also show that it is superior to the conventional combination method, majority voting method.     

基于信息融合的罪案关联证据推理模型

针对传统犯罪案件中出现的冲突证据难以处理的情况,提出一种基于证据可信度和灰色关联的冲突证据融合处理方法;基于相似性视角的灰色关联度作为证据间的联系,采用改变证据源的证据组合规则来衡量证据源中各个证据之间的贴近度;考虑到证据的可信度,提出新的基于证据贴近度的权重确定方法,以证据的可信度作为权重,对参与融合的证据的基本概率分配函数进行加权平均,使证据融合收敛速度更快,并提升融合效果.最后以安徽省某市的入室盗窃犯罪案件为例,运用基于信息融合的关联证据推理方法处理案件中的冲突证据问题,验证了所提出方法的合理性和有效性.     

基于综合云的不确定语言多准则群决策方法

针对准则权重已知、决策者权重未知、准则值为不确定语言的多准则群决策问题,提出一种基于云模型的决策方法.该方法首先将不确定语言值转化为综合云;然后采用生成浮动云的方法进行偏好集结,并通过计算＂不确定度＂和＂决策者偏差度＂求得决策者权重;最后引入Hamming距离求得贴近度大小,通过比较得到方案集的排序.实例分析表明了该方法的有效性和可行性.     

基于模糊理论的多传感器信息融合算法

D-S证据理论作为一种重要的不确定性推理理论,为处理传感器信息的模糊性及不确定性提供了很好的解决方法。但各个证据中的基本概率分配函数（mass函数）如何生成,仍是人们需要解决的问题。针对这一问题,提出了一种基于模糊理论中的高斯隶属度函数来得到传感器提供信息的可信度,计算了各个传感器之间的相互支持度;将各传感器的可信度和支持度转化成mass函数;利用证据理论对多传感器信息进行融合。仿真试验表明该方法能够有效提高识别的准确性和可靠性。