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.

D-S理论和Markov链组合的桥梁性能退化预测研究

为准确预测桥梁性能退化,考虑到数据随机性和微小扰动发生状态跳跃,提出了一种D-S(Dempster-Shafer)证据理论和Markov链组合的桥梁性能退化组合预测模型和性能退化率的概念.该模型基于指数平滑(exponential smoothing,ES)方法获得新的预测数据序列,并利用Markov链和D-S理论不断进行优化,从而实现桥梁性能退化的组合预测.实际工程的应用结果表明:性能退化率可以直观地表征在梁性能退化的速度.其次,该模型的平均相对误差为1.54%,较于回归、灰色和模糊加权Markov链模型,精度分别提高了1.11%,0.88%和2.8%,而后验差比值为0.242,小于0.35;模型的标准差为9.021,相比其他模型分别减小了3.978,3.405和7.500,而变异系数为0.109,均小于其他模型,验证了组合预测模型在精度和稳定性方面的优越性,可为在役桥梁结构性能退化预测与维护提供理论基础.

Evidence combination method in time domain based on reliability and importance

In order to achieve the information fusion in the time domain based on the evidence theory, an evidence combination method in the time domain based on reliability and importance is proposed according to the idea of evidence discount. Firstly, the distortion of the time-domain evidence is judged based on single exponential smoothing. The real-time reliability of the evidence at the adjacent time is obtained by the real-time reliability assessment method of the evidence based on the credibility decay model.Then, the relative importance of the evidence at the adjacent time is obtained by comprehensively considering improved conflict degree and uncertainty. Finally, based on the criterion of evidence discount and the Dempster’s rule of combination, the evidence combination is carried out to achieve the sequential combination of time-domain evidence. The numerical simulation and analysis show that this method has fully embodied the dynamic characteristics of time-domain evidence combination, and it has strong processing ability for conflict information and anti-disturbing ability.The proposed method has good applicability to information fusion in the time domain.

Temporal evidence combination method for multi-sensor target recognition based on DS theory and IFS

In order to effectively deal with the conflict temporal evidences without affecting the sequential and dynamic characteristics in the multi-sensor target recognition(MSTR) system at the decision making level, this paper proposes a Dempster-Shafer(DS) theory and intuitionistic fuzzy set(IFS) based temporal evidence combination method(DSIFS-TECM). To realize the method,the relationship between DS theory and IFS is firstly analyzed. And then the intuitionistic fuzzy possibility degree of intuitionistic fuzzy value(IFPD-IFV) is defined, and a novel ranking method with isotonicity for IFV is proposed. Finally, a calculation method for relative reliability factor(RRF) is designed based on the proposed ranking method. As a proof of the method, numerical analysis and experimental simulation are performed. The results indicate DSIFS-TECM is capable of dealing with the conflict temporal evidences and sensitive to the changing of time. Furthermore, compared with the existing methods, DSIFS-TECM has stronger ability of anti-interference.

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.

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.

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.

Expert system for earthquake prediction（ESEP）（2）－Knowledge representation and evidence combination

This paper deals with knowledge representation of ESEP (Expert System for Earthqauke Prediction). Attending the characteristics of the knowledge in earthquake prediction domain, production representation and procedural representation are connected in the knowledge repesentation model of ESEP named ESEP/K, and three new ways of evidence conbination are proposed for production rules besides 'AND' and 'OR'.

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

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。

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.

基于迭代合成的D-S证据理论改进方法

针对D-S算法中折扣因子不能准确度量证据重要性和一次合成不够精确等问题进行了研究,提出一种基于迭代合成的D-S改进算法。该算法使用复合折扣因子进行证据重要性度量,并用融合结果不断修正复合折扣因子,通过多轮迭代合成最终结果。最后用模拟算例验证了算法具有很好的融合效果,算法的迭代次数不会随着融合精度的提升而激增,也不会随着证据规模的增大而增多,说明算法是有效的、稳定的和可扩展的,适用于大规模证据合成。

D-S证据理论中冲突处理的新方法

针对经典D-S证据理论在处理高度冲突的证据信息时会产生有悖常理结论的问题,提出了一种新的证据合成方法。通过加性策略计算冲突焦元的支持信度,把证据中冲突焦元的支持信度作为新的基本概率分配,通过两两依次合成的方法实现多证据的合成。实验结果表明,该合成方法能够有效解决零元悖论问题,且在合成的结果的准确性、区分能力和适应性等方面优于现有典型方法。

D-S证据推理改进方法综述

D-S证据理论可以有效地处理不确定信息,是有效的数据融合方法之一,但在证据高度冲突时,因其归一化过程会产生有悖常理的无效结果。针对此问题,大家提出了许多不同的改进方法,基本上可以分为两类:修改证据及修改组合规则。论文总结了国内外典型文献并进行系统条理的分析,收集各方研究方法并逐步深入压缩提炼形成了一套系统清晰的解决相关问题的思路。并通过实例,比较检验了几种改进方法的有效性。

一种解决D-S理论证据冲突的有效方法

D-S证据理论是一种有用的不确定性推理方法,由于证据合成公式存在不足,影响了证据理论的应用。针对有效融合高度冲突的证据,提出了一种新的有效加权冲突证据组合方法。该方法通过引入一个证据体距离函数,利用证据之间的相对距离确定了证据的权重,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。与其他方法比较,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,实例表明了该方法的有效性。

一种新的基于证据权重的D-S改进算法

D-S(Dempster-Shafer)证据理论是一种有效的不确定性推理方法,但在组合高冲突证据时,D-S证据理论得到的结果却往往有悖常理。为了解决冲突证据的合成问题,考虑到不同的证据在合成过程中的重要程度不同,提出了一种新的基于证据权重的D-S改进算法。该方法首先引入一个度量证据体间相似度的证据距离函数,建立相应的证据距离矩阵,求出系统中各证据到证据集的平均欧式距离,然后通过信任函数来获得描述各证据重要程度的权重系数并对证据源进行修正,最后利用D-S组合规则对修正后的证据进行合成。通过算例的分析以及与其它改进算法的比较,验证了新方法的有效性和优越性。

基于D-S证据理论的网络安全风险评估模型

针对网络信息系统的安全风险评估问题,提出了基于D-S证据理论的风险评估模型。证据理论是一种处理不确定性的推理方法。首先用"折扣率"对Dempster合成法则进行了改进,然后使用改进后的Dempser合成法则对网络中存在的各种风险因素进行合成,减少了风险因素中的不确定性,并以实例验证了该模型在网络安全风险评估中的应用,仿真结果证明了该算法的正确性。最后,通过与模糊综合评判法进行比较验证了证据理论具有更高的准确性。

D-S证据理论中一种新的冲突证据融合方法

针对D-S证据理论在融合高度冲突的证据时可能导致与直观结果相悖的问题,通过分析现有的几种冲突证据融合方法,提出了一种有效处理冲突证据的融合方法。该方法综合了模型修正和规则修正这两类方法的优点。首先基于冲突系数矩阵计算证据的可信度,并以此作为折扣因子来修正证据结构,然后基于局部冲突、局部分配原则确定证据合成时的冲突分配空间和分配权重。实验结果表明,该方法对合理的合成方向具有较快的收敛速度,同时降低了决策风险。

D-S证据理论在证券投资中的应用

D-S证据合成公式要求待合成的证据是相互独立且证据之间不能有较强的冲突,否则合成结果将有悖常理,因此,冲突证据合成前往往要进行修正.该文提出了一种基于修正系数学习的修正方法,同时将证据理论引入证券市场,将每个专家的预测意见视为证据,将不同专家预测意见的综合视为证据合成问题,然后用该文提出的修正方法对冲突专家意见进行修正,以取得较好的合成效果,从而为证券投资者提供有价值的参考信息.