A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

基于D-S证据理论改进AHP-熵权的流域洪涝灾害评估研究

考虑致灾因子危险性、孕灾环境敏感性以及承灾体易损性,选取指标构建小清河流域洪涝灾害风险评估指标体系,提出一种基于D-S证据理论的改进AHP-熵权法计算指标权重,求取洪涝灾害风险指数,运用自然断点分级法确定洪涝灾害风险等级,分析小清河流域洪涝灾害风险空间分布情况。结果表明:小清河流域洪涝灾害风险总体上表现出南低北高的趋势,其中高风险区和较高风险区分别占流域面积的8.7%和14.3%,主要分布在小清河干流以及主要支流两岸。所得评估结果同“利奇马”台风发生期间实际洪灾风险分布情况一致,对比证明基于D-S证据理论的改进AHP-熵权法优于AHP和熵权法,可为小清河流域防洪减灾决策提供依据。

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

New weighting factors assignment of evidence theorybased one vidence distance

Evidence theory has been widely used in the information fusion for its effectiveness of the uncertainty reasoning. However, the classical DS evidence theory involves counter-intuitive behaviors when the high conflict information exists. Based on the analysis of some modified methods, Assigning the weighting factors according to the intrinsic characteristics of the existing evidence sources is proposed, which is determined on the evidence distance theory. From the numerical examples, the proposed method provides a reasonable result with good convergence efficiency. In addition, the new rule retrieves to the Yager's formula when all the evidence sources contradict to each other completely.

基于改进D-S证据和融合权集的安全风险评估

针对信息化系统安全风险评估过程中安全风险因素指标的重要性难以赋权的问题,本文以建筑工地施工现场为应用场景,提出一种基于改进的D-S证据理论与融合权集结合的安全风险评估模型.首先,在充分研究建筑工地安全风险评估流程和要素的基础上,建立建筑工地安全评价体系;其次,采用基于权值分配和矩阵分析的D-S合成算法改进AHP法和基于数据的熵权法计算评价体系中指标层中各指标的主、客观权重;然后,运用改进的D-S证据融合算法进行多源证据的合成,获取指标权重,避免单一赋权的片面性,得到最优综合权重;最后,根据TOPSIS评价算法计算建筑工地综合评价指数.分析表明,基于改进D-S证据理论-融合权集的安全风险评估模型,能有效评估建筑工地施工现场的安全性,降低评估结果的不确定性,提高风险评估结果可信度.

Real-Time Update of Sequence Placement Logic for High Arch Dams Based on Evidence Weight Discount

Sequence placement logic plays a significant role in construction simulation of high arch dams and directly affects the simulation process and results.To establish a sequence logic for dam block placement,the construction scheme,real-time construction process,and random factors of the site all need to be considered in detail.There are few studies available currently that take all these factors into consideration.To address this problem,a real-time update of sequence placement logic for high arch dams based on evidence weight discount is proposed in this study.First,the subjective weight of the dam block sequence priority criteria is built using a consistent matrix method based on the construction scheme.Second,using evidence theory,dynamic objective weight of the priority criteria and basic probability assignment is built.Finally,using a weight self-adaptive adjustment method and comprehensive evidence discounting,the placing probabilities of different dam blocks are obtained.A case study indicates that this method can realize realtime update of sequence placement logic.

D-S证据理论中冲突证据的合成方法

针对高度冲突证据的合成问题,分析了D-S合成方法及其改进方法的不足,提出了一种新的改进方法。新方法仍利用D-S合成公式,但针对该公式要求参与合成的各证据需具有相同重要程度的要求,考虑到融合系统中各证据的重要程度可能不同,直接利用公式合成可能得到与实际相悖的结论,因而引入证据权的概念,给出了证据权的具体算法,通过证据权对已有的基本可信度分配函数进行修正得到了重要性相同的新的证据的基本可信度分配模型。最后通过算例比较表明新方法的合成结果更为理想。

一种改进的D-S证据理论合成方法

针对D-S证据理论计算量大和不能有效合成高度冲突证据的不足,提出一种基于加权和矩阵运算的证据组合方法。该方法通过引入权重系数区分不同证据在数据融合中的重要程度,因此能有效合成高度冲突的证据。同时,在证据合成过程中通过引入矩阵运算,有效地降低了算法的计算量。仿真结果表明,与传统的基于D-S证据理论的多传感器数据融合方法相比,改进方法不但能提高证据合成的可靠性和准确性,而且具有较小的计算量。

基于熵权灰色关联和D-S证据理论的威胁评估

为了更好地处理空中威胁目标的不确定性信息,提出了基于熵权灰色关联和D-S证据理论相结合的威胁评估方法。将熵理论应用于求解各指标权重,利用灰色关联法确定各指标的不确信度,进而得到各指标下不同目标的Mass函数,通过D-S证据理论对各Mass函数进行合成,根据置信函数大小对目标进行排序。仿真实验证明该方法是合理有效的。

一种有效解决D-S理论冲突证据合成的方法

针对标准D-S证据理论中存在的冲突证据合成问题,提出了一种冲突证据加权的方法。该方法将各个传感器的基本概率赋值映射到多维空间中的某个点,计算任意两点的欧氏距离,利用平均距离确定证据的权重,采用哈夫曼树对证据加权平均后再利用D-S合成规则实现信息的融合。实例论证了该方法的有效性,它能有效解决冲突证据合成的问题。

基于粒子群寻优的D-S算法

D-S证据理论是一种性能优越的信息融合方法,由于各传感器所提供的证据的重要程度不同,需要对各证据进行加权合成处理。目前的加权D-S证据理论限于合成规则的研究,较少讨论如何获取优化的证据权值。实际上,证据权值的确定是证据进行加权合成的基础和关键。针对加权证据理论的这一研究不足,提出了一种求取最佳证据权值的方法。首先,阐述了思想,再建立了优化模型;然后,改进了粒子群优化求解算法,利用其优越的求解非线性多峰值函数的能力,求解出了最优权值。通过实例仿真表明:这种证据理论的加权算法是有效的,与对比方法相比,具有更好的融合效果。

基于权值D-S证据理论的车辆导航地图匹配

对传统D-S证据理论进行了改进,解决了证据的可信度问题。该改进算法首先根据证据的局部决策算出局部决策值,构造整个系统的支持矩阵,并求出支持矩阵的特征向量,以此作为各个证据的可信度;然后把可信度作为各个证据的权值,以此修正D-S证据的融合算法,使新的组合理论规则能够有效地处理证据中的冲突信息;最后结合当前城市道路网日益复杂的实际特点,把基于权值的D-S证据理论应用于车辆导航的地图匹配中,建立车辆位置信息和方向信息判断规则。实车试验结果表明:应用该算法可以较好地解决城市复杂路网地图匹配问题。

基于D-S证据理论的短期负荷预测模型融合

在各种预测模型融合时确定各种模型的权重直接影响到预测精度。对3种不同的神经网络负荷预测模型分别建立了权重提取和权重融合的数学模型,并运用证据理论对3种预测模型的权重进行融合。通过对历史预测数据的分析,提取了证据理论的融合样本,并将信度函数的多重融合结果作为负荷预测模型权重,得到权重融合后待预测日的负荷预测结果。将权重融合模型的预测结果与单一模型的预测结果进行比较,结果表明权重融合后的模型具有更高的预测精度,提高了负荷预测的准确性。

基于变权重和D-S证据理论的桥梁安全评估

桥梁结构的安全评估受环境侵蚀、材料老化、外部荷载等多源因素的影响,采用单一指标评价桥梁的安全性容易产生误判.因此,提出一种基于信息融合的多因素桥梁安全评估方法.该方法首先确定每种因素的变化区间,通过多工况数值计算提取典型的力学指标,利用信息熵和模糊层次分析法确定指标的变权重及单指标的等级划分原则并转化为基本概率指派函数.采用Dempster组合规则确定融合后的安全等级,从而建立桥梁的安全评估先验数据库.将该方法应用于预应力连续梁桥的安全评估中,结果表明该评估方法客观、合理,具有一定的工程实用性.

改进的D-S证据理论在战场目标识别中的应用

战场目标识别中多传感器信息融合面临各种不确定冲突信息。为此,提出了一种基于权重的证据调整方法,给予证据分配不同的权重,重新修正基本概率分配函数,利用Dempster组合规则实现信息融合。通过数值验证并与其他的改进算法对比,结果更为理想,能有效的减小冲突证据的不良影响,同时具有较高的收敛速度,降低了决策风险,仿真结果表明,算法有效、可行。

D-S证据理论中一种冲突证据的融合方法

针对D-S证据理论对于高冲突证据融合失效的问题,基于证据加权平均处理的方法,对D-S算法进行了改进。提出了一种新的相似度函数来表示证据的相似度,并通过归一化处理得到证据的支持度,对证据进行预处理,再采用Dempster规则融合证据。通过数值验证并与其他的改进算法对比,结果更为理想,它能有效地减小伪证据的不良影响,同时具有较高的收敛速度,降低了决策风险。对新的相似度函数进行分析,证明了改进算法的合理性。

加权优化的D-S证据理论在大坝安全评价中的应用

针对传统D-S证据理论存在鲁棒性差与"一票否决"等问题,提出采用加权融合法优化D-S证据理论,以提高其在大坝安全评价应用中的可靠性。首先对原始监测数据进行标准化及归一化处理;然后用欧氏距离确定权重,利用各监测项目间的内在联系进行D-S证据融合;最后根据加权融合结果对西溪水库大坝进行安全评价。结果表明,大坝整体的安全与较安全指标概率赋值达0.527,远大于较不安全指标0.049,大坝处于安全状态;坝基渗流较不安全概率赋值达0.492,存在一定的渗流安全隐患,融合评价结果与工程实际相符合,说明该方法可如实反映大坝的安全状况。

基于D-S理论的多传感器目标识别能力分析

通过分析待识别目标类型与结果类型的差别,确定以目标正确识别概率、目标错误识别概率和目标无法识别概率来度量传感器的目标识别能力。鉴于各传感器的识别结果概率之间的证据冲突,给出了一种通过与准理想结果的距离大小来确定信任权重的方法,并依据信任权重重新调整各证据的概率分配,实现冲突证据的预处理,然后使用Dempster规则对各传感器的识别结果概率进行组合,从而确定了多传感器的目标识别能力的度量模型,实例证明给出的目标识别能力度量方法是有效可行的。