基于城市街区距离的证据融合方法

针对传统Dempster-Shafer证据合成方法合成冲突证据时,会出现相悖的结论的问题,提出了基于城市街区距离的冲突证据合成方法.方法利用证据冲突信息,通过城市街区距离得到证据距离,距离表征证据的冲突程度,并进一步计算得到权重系数,根据权重系数对证据进行重新概率分配后,完成证据预处理过程,最后用改进的合成规则,完成再分配证据的合成.实例合成验证了算法在合成冲突证据方法具有最优结果,且和推理一致,可以通用于证据合成.     

采用聚类分析的冲突证据判定及合成方法

针对D-S 证据理论中高度冲突证据合成时产生反直观结果的问题,从聚类分析的角度提出了一种新的冲突证据合成方法。首先建立余弦相似度空间,利用证据向量之间的夹角余弦定义冲突证据检测因子,进而提出冲突证据判据判定冲突证据,从而实现证据分类;其次引入冲突比例因子决定冲突证据的修正方式,通过绝对相似度对冲突证据进行局部修正,通过相对相似度对证据进行全局修正;最后将修正后的证据带入D-S 公式进行合成。应用实例证明:新方法能够判定并有效合成冲突证据,具有较好的分类精度、收敛速度和稳定性。     

一种新的基于证据理论的合成公式

由于D-S证据合成公式所存在的不足,使证据理论的应用受到了一定的限制.Yager对此作了改进,但改进后合成公式又存在着新的问题.鉴于此,本文在引入证据可信度概念的基础上,提出了一个新的证据合成公式.新的合成公式弥补了D-S证据理论和Yager合成公式所存在的不足,使冲突证据合成的结果更为理想.     

一种新的证据距离表示方法

在冲突证据合成之前,确定证据的冲突程度是必须的。为了更加准确地表示证据的冲突程度,提出了一种新的证据距离表示方法。方法以2种方式相反的冲突表示系数为基础,通过KL融合得到两者距离,进一步通过提出的距离公式,最终得到证据距离。通过具体的冲突证据实验,使得到的证据距离最大,且采用提出算法使得到的证据距离来判断冲突证据与结果,实际结果一致。新的证据距离可以很好地表示证据间的冲突程度,可以进一步处理用于冲突证据的合成。     

一种基于冲突重新分配的D-S证据合成方法

D-S证据理论是一种处理不确定性问题的有效推理方法,但证据合成存在高度冲突问题。考虑到直接利用公式合成可能得到与实际相悖的结论,故分析了D-S合成方法及其改进方法的不足,引入冲突系数和分配系数,给出了具体算法,通过分配系数对基本概率赋值函数进行修正,提出了一种基于冲突重新分百己的合成方法。最后,通过实例比较表明,该方法的合成结果较为理想。     

基于证据源和组合规则双修正的证据合成

针对冲突证据合成时传统证据合成和单一修正改进方法存在的问题,提出了新的证据源预处理和组合规则双修正的证据合成方法.利用夹角余弦理论,计算证据向量距离后转化得到权重系数,依据权重系数对证据进行重新概率分配,完成证据源修正;之后用修正的动态合成规则,进行修正证据的合成;同时提出了算法分析模型.实例结果验证了算法在证据合成时具有最优结果、较强通用性和稳定性,可应用于信息融合、不确定信息决策等领域.     

基于改进D-S证据理论的数据融合目标分类

首先采用分类算法对MSTAR数据集进行十类目标分类识别、三类目标的变体分类识别,然后根据分类调参过程中的先验知识修正证据即分类器输出,构造基本置信函数,并采用改进的合成规则即基于冲突系数K和Pignistic概率距离相结合的冲突度量方法,对冲突证据采用按比例分配冲突度的合成规则进行合成。未融合时,三类目标的变体分类准确率最高为93.553%,融合后三类目标变体分类识别率为95.092%,提升幅度约是理想状态的37%。     

新的证据冲突衡量标准下的D-S改进算法

 针对传统证据冲突衡量标准存在的不足及高冲突下Dempster证据组合规则失效的问题,通过pignistic变换,定义了新的证据冲突衡量标准,基于此,提出了一种新的D-S改进算法.该方法依据少数服从多数的决策思想,引入了描述证据重要度的权重系数,对证据进行预处理,再采用Dempster规则进行组合.通过仿真算例分析,并与其它改进方法进行对比,验证了新算法在处理证据冲突方面的性能显著改进,加快了收敛速度,同时降低了决策风险.     

基于K-L距离的改进D-S证据合成方法

提出了一种证据距离为基础的冲突证据合成方法.通过改进的K-L距离得到证据间间距,进一步通过相关算法转化为证据权值,权值反映了证据的重要程度,以权值为系数对原始的基本可信度分配函数进行重新分配,再通过改进的合成公式,得到证据合成结果.最后通过具体的算例比较,说明新方法的合成结果更确定,更符合实际情况.     

基于皮尔逊系数的冲突证据合成新方法

提出了一种融合证据源预处理和合成规则修改的冲突证据合成方法.算法得到两两证据源间的皮尔逊系数,每个证据源具有特有皮尔逊系数值和.以具有最大皮尔逊值和的证据源为中心证据源,所有证据源相比中心证据源的比例为该证据源的权值,权值反映了证据的重要程度,从而冲突证据源具有最小的权值.利用权值对原始证据源进行预处理,降低冲突证据的重要性.再通过改进的合成规则,对处理后的证据源进行合成,得到最终合成结果.证据正常与冲突情况下的仿真算例验证了新方法不但在证据合成中具有很高的通用性,而且相比其他方法具有很强的优越性.     

Weighted evidence combination based on degree of credibility and certainty

To overcome the invalidation problem of Dempster rule with high conflict,a weighted combination method based on degree of credibility and certainty was proposed.Firstly,the cosine similarity was modified to hold the ability to measure the evidence conflict when multi-subset focal elements were included,followed by the building of evidence credibility model.Secondly,the evidence certainty model based on precision and entropy was presented,which can both reflect the evidence’s degree of multi-subset focal elements and the dispersion degree of probability assignment.Then the weighted coefficient was determined by credibility and certainty.Finally,the normalized weighted coefficient was used to average the basic probability assignment,and the final combination result can be obtained according to Dempster rule.Numerical examples show that,compared with other traditional weighted combination methods,the proposed approach has made better performance in reducing conflict and accelerating convergence.     

Dempster–Shafer theory-based human activity recognition in smart home environments

Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.     

基于D-S证据理论的数据关联新方法

针对被动传感器网络中的多目标数据关联,提出一种基于D-S证据理论的数据关联新方法。根据被动传感器网络中多目标跟踪的特点,建立适合于被动目标数据关联的6种合成证据（包含速度变化量、航向角变化量、飞行时间变化量、观测可能性、属性变化量与未知）,根据各自的自相关函数自适应确定证据权值,通过证据权值与信任函数的乘积动态调整合成中各证据的比重,最后应用多级决策获取对目标观测的关联结果。仿真结果表明,使用自相关函数改进的D-S证据理论的数据关联新方法能够在抑制观测中冲突证据影响的同时,削减关联运算时间,获得被动多目标实时有效的关联结果,性能优于模糊C均值聚类关联方法（FCM）和传统D-S证据理论的数据关联方法。      

基于D-S证据理论的多模复合制导目标识别

多传感器信息融合已成为现代条件下目标识别的重要手段.研究基于D-s证据理论的多传感器信息融合算法,提出一种基于Dempster-Sharer理论的推广方法以解决融合信息的证据冲突问题,并将其用于红外成像/毫米波复合制导系统,进行目标识别的决策信息融合,结果比较理想,能够有效地化解证据间的冲突问题.     

一种有效处理冲突证据的组合方法

为了有效融合高度冲突的证据,提出一种新的基于证据距离的组合方法,首先引入了一个证据体距离函数,之后求出系统中各证据被其它证据所支持的程度,对支持度归一化后获得各个证据的可信度,并将可信度作为证据的权重,对系统中的证据加权平均后再利用Dempster组合规则实现信息融合,与其它方法比较,该方法在系统存在伪证据(干扰)时仍然能够有效快速地识别出目标。     

Evidence combination method based on Pignistic function transformation and correlation coefficient

The situation of high conflict between evidences and one ballot veto cannot be solved by classical D-S rule,and the results obtained from classical D-S rule are contrary to the facts.To solve this problem,a new standard to measure conflicts between evidences was proposed based on the combination of Pignistic function transformation and correlation coefficient,and also a novel kind of weighted combination method which was applied to measure conflicts between evidences was put forward according to the standard.After that,a support matrix was constructed based from which the credibility of evidence was obtained,and the weighted average method was used to revise the evidence.Finally,the combination was accomplished by using Dempster’s rule.The result of numerical examples shows that it’s effective to solve the combination of conflicting evidence.Compared with other methods,the proposed method has good astringency.     

A non‐biased trust model for wireless mesh networks

Trust models that rely on recommendation trusts are vulnerable to badmouthing and ballot‐stuffing attacks. To cope with these attacks, existing trust models use different trust aggregation techniques to process the recommendation trusts and combine them with the direct trust values to form a combined trust value. However, these trust models are biased as recommendation trusts that deviate too much from one's own opinion are discarded. In this paper, we propose a non‐biased trust model that considers every recommendation trusts available regardless they are good or bad. Our trust model is based on a combination of 2 techniques: the dissimilarity test and the Dempster‐Shafer Theory. The dissimilarity test determines the amount of conflict between 2 trust records, whereas the Dempster‐Shafer Theory assigns belief functions based on the results of the dissimilarity test. Numerical results show that our trust model is robust against reputation‐based attacks when compared to trust aggregation techniques such as the linear opinion pooling, subjective logic model, entropy‐based probability model, and regression analysis. In addition, our model has been extensively tested using network simulator NS‐3 in an Infrastructure‐based wireless mesh networks and a Hybrid‐based wireless mesh networks to demonstrate that it can mitigate blackhole and grayhole attacks.     

基于相关系数的证据冲突度量方法

当证据高度冲突时,使用D-S组合规则将会得到不合常理的结果,因此,针对证据组合规则的改进成为一个重要的研究课题,在选择合适的证据组合方法之前,确定证据之间是否冲突、冲突程度如何,是个至关重要的问题,为了更加准确地描述证据间的冲突,在分析证据相似度与证据冲突关系的基础上,基于证据相关系数提出了一种新的证据冲突度量方法,理论分析和数值计算表明该方法可有效表示证据之间的冲突程度,相关系数越大,证据间冲突就越小,与其他方法相比,该方法更具全面性和合理性。     

基于D—S证据理论的多传感器雷达体制识别

在阐述Dempster-Shafer(D-S)证据理论的基础上，较系统地论述了基于D－S证据理论的多传感器雷达体制识别的数据融合方法，并给出了具体的识别实例。     

多传感器数据融合非合作目标识别技术研究

采用Dempster-Shafer证据推理法，融合判决算法，在模拟的单站多传感器系统中，进行计算机仿真，仿真结果证实所采用的技术可以有效提高目标识别的可信度。