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.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

基于D-S-CBR的燃料电池系统生产过程故障诊断

氢能产业发展迅速,质子交换膜燃料电池系统已实现初步应用广,在车、船、电站领域的应用逐渐推广。由于燃料电池系统的产业化开始时间有限,产品标准化程度较低,在生产过程中容易出现各种故障问题,研发、生产人员难以定位故障点及故障原因,排查及解决故障的时间长,严重影响生产进度。本研究通过将证据融合理论与案例推理故障诊断方法结合,构建一种混合数据的D-S-CBR系统,实现两种类型异构数据的融合及故障诊断。并将此研究应用于燃料电池系统的生产中,辅助研发、生产人员进行生产过程故障诊断,减少故障诊断时间,降低生产成本,提高产品质量。

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.

EARLY WARNING MODEL OF NETWORK INTRUSION BASED ON D-S EVIDENCE THEORY

Application of data fusion technique in intrusion detection is the trend of next- generation Intrusion Detection System (IDS). In network security, adopting security early warn- ing technique is feasible to effectively defend against attacks and attackers. To do this, correlative information provided by IDS must be gathered and the current intrusion characteristics and sit- uation must be analyzed and estimated. This paper applies D-S evidence theory to distributed intrusion detection system for fusing information from detection centers, making clear intrusion situation, and improving the early warning capability and detection efficiency of the IDS accord- ingly.

Ubiquitous Computing Identity Authentication Mechanism Based on D-S Evidence Theory and Extended SPKI/SDSI

Ubiquitous computing systems typically have lots of security problems in the area of identity authentication by means of classical PKI methods. The limited computing resources, the disconnection network, the classification requirements of identity authentication, the requirement of trust transfer and cross identity authentication, the bi-directional identity authentication, the security delegation and the simple privacy protection etc are all these unsolved problems. In this paper, a new novel ubiquitous computing identity authentication mechanism, named UCIAMdess, is presented. It is based on D-S Evidence Theory and extended SPKI/SDSI. D-S Evidence Theory is used in UCIAMdess to compute the trust value from the ubiquitous computing environment to the principal or between the different ubiquitous computing environments. SPKI-based authorization is expanded by adding the trust certificate in UCIAMdess to solve above problems in the ubiquitous computing environments. The identity authentication mechanism and the algorithm of certificate reduction are given in the paper to solve the multi-levels trust-correlative identity authentication problems. The performance analyses show that UCIAMdess is a suitable security mechanism in solving the complex ubiquitous computing problems.

Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory

According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.

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.

A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

基于D-S证据推理的车辆导航系统地图匹配算法

研究地图匹配算法中的匹配道路的选择问题 ,提出基于 D- S证据推理理论的地图匹配算法 .根据 D- S证据推理的基本原理 ,结合车辆行驶情况 ,给出当前时刻车辆位置信息和方向信息的基本概率分布函数的设计方法 .按照 D- S合成公式 ,对位置和方向信息进行融合 ,并根据融合结果选择匹配道路 .在设计方法中 ,引入位置信息和方向信息的可靠性参数 .仿真结果表明 ,该方法是行之有效的 ,通过适当地调整可靠性参数的值 。

基于改进D-S证据理论的车辆导航地图匹配

对传统D-S证据理论算法进行了改进,解决了证据的可信度和决策规则问题,使新的组合推理规则能够有效地处理证据中的冲突信息。并结合当前城市道路网日益复杂的实际特点,采用Pignistic概率的决策理论和基于权值D-S证据推理的地图匹配新算法,建立车辆位置信息和方向信息判决规则,有效地利用现有信息扩大各匹配道路之间的信度差异,提高了算法的鲁棒性。通过仿真和试验表明,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推理的模糊模式识别方法

针对战场环境下数据融合系统的ESM设备获得的辐射源信息在时间上的冗余性,提出了一种基于D-S证据推理的模糊模式识别方法。该方法通过对观测样本信任度(模糊隶属度)的重新分配,有效地解决了辐射源信息不确定性问题,提高了辐射源的正确识别率。最后通过单一的模糊识别和D-S证据组合的模糊识别两种方法的仿真对比实验验证了后者的有效性。

基于D-S推理的灰关联雷达辐射源识别方法研究

在战场实际环境中,多数情况下传感器设备获得辐射源的信息在时间上是冗余的。针对这种实际侦测环境,基于信息融合的思想,提出了一种新的辐射源识别方法。该方法在灰关联识别的过程中,引入 D S证据理论,通过对信号的积累和对信任度的重新分配,对识别结果进行了有效的融合,提高了辐射源的正确识别率。仿真结果表明了该方法的可行性和有效性,为工程应用提供了参考。

基于D-S证据理论的分布式入侵检测方法研究

入侵检测系统是信息安全的一种重要手段,该文提出了一种基于D-S证据理论的分布式入侵检测系统模型,讨论了其实现方法,并进行了仿真实验,仿真实验表明经过多传感器数据融合后得到的结果优于单传感器得到的结果。

多传感器模糊D-S理论辐射源识别

研究了将D-S证据理论与模糊推理组合应用辐射源识别的方法,提出了基于模糊综合评估获取基本概率分配函数的方法;在此基础上研究了应用D-S证据理论进行多传感器信息融合,从而识别雷达辐射源的原理;仿真实验和对比实验表明本算法的有效性。

一种基于D-S证据理论的复杂产品模块划分评价方法

模块划分是复杂产品模块化设计的基础,划分的优劣直接关系到客户定制产品设计、制造和装配的效率。针对现有的研究在模块划分方案形成以后缺乏对模块优劣的合理评价,该文通过引入D-S证据理论,从装配复杂性、可制造性、模块性、稳定性、造型结果与体积紧凑性6个指标对模块创建方案的满意度进行不确定和不完全性的综合评判,提出了一种双层模块化创建方案评价方法。该方法符合人的思维判断过程,具有一定的灵活性、有效性和合理性。

D-S证据理论和本体推理互补的活动识别方法

有效的活动识别是智能辅助的关键。结合D-S证据和本体推理,提出一种互补结构的活动识别方法。该方法通过在证据理论和知识库之间建立对应关系形成互补,既解决了异构数据之间的知识共享,又对本体推理的规则冲突进行了处理,提高推理结果的准确性。通过复杂活动实例在原型系统中的应用,验证了该方法的可行性和有效性,并且能够有效地提高活动识别准确率。

D-S证据理论在便携式室内环保监控仪中的应用

为解决便携式室内环保监控仪在信息处理中的不确定性问题,提出一种改进的基于D-S证据理论的多传感器数据融合算法及融合结果的目标判定原则。该算法和判定原则以室内空气监测数据为研究对象,对室内空气质量进行等级评估,并根据评估结果控制室内环保监控仪动作。试验表明,改进的D-S证据理论的数据融合算法和判定结果是正确的,以此构造的智能室内环保监控系统具有可靠性高、鲁棒性好的优点。