A networked transferable belief model approach for distributed data aggregation

This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.     

一种基于可传递置信模型的分布智能体决策融合方法*

在分析与研究对抗性多机器人系统决策问题的基础上,提出了一种基于可传递置信模型的多智能体决策融合方法;构建了决策融合体系架构,分别设计了基于证据推理的观测智能体模型,基于TBM的决策智能体模型以及决策融合中心模型,给出了相应的算法。通过在机器人足球中的应用及仿真实验,体现了本方法在对抗性多机器人系统中决策制定的良好性能及效果。     

Diagnostic reasoning under conflicting data: the application of the transferable belief model

One of the issues in diagnostic reasoning is inferring about the location of a fault in cases where process data carry inconsistent or even conflicting evidence. This problem is treated in a systematic way by making use of the transferable belief model (TBM), which represents an approximate reasoning scheme derived from the Dempster–Shafer theory of evidence. The key novelty of TBM concerns the paradigm of the open world, which turns out to lead to a new means of assigning beliefs to anticipated fault candidates. Thus, instead of being ignored, inconsistency of data is displayed in a portion of belief that cannot be allocated to any of the suspected faults but rather to an unknown origin. This item of belief is referred to as the strength of conflict (SC). It is shown in this paper that SC can be interpreted as a degree of confidence in the diagnostic results, which seems to bring a new feature to diagnostic practice. The basics of TBM are reviewed in the paper and the implementation of the underlying ideas in the diagnostic reasoning context is presented. An important contribution concerns the extension of basic TBM reasoning from single observations to a batch of observations by employing the idea of discounting of evidence. The application of TBM to fault isolation in a gas–liquid separation process clearly shows that extended TBM significantly improves the performance of the diagnostic system compared to ordinary TBM as well as classical Boolean framework, especially as regards diagnostic stability and reliability.     

多传感器数据的统计融合方法

在多传感数据融合过程中 ,各传感器的可靠程度的确定是至关重要的。利用统计方法理论 ,将各传感器的可靠程度模糊化 ,进而给出各传感器的综合支持程度指标 ,并在此指标基础上给出多传感器数据的融合结果。该方法计算简便 ,其结论较为稳定     

多传感器数据融合的一种方法

在多传感器数据融合的过程中,首先必须验证各个传感器的可靠程度,即确立正确的关系矩阵。一般做法是根据门限值判断两个传感器是否相互支持,或者用分段直线等方法表示其支持程度。在分析的基础上,提出了应用椭圆曲线表示支持程度,并分别给出了融合结果。     

基于PDA 思想的分布式多传感器融合方法

在传感器观测噪声不一致或有异常数据存在的条件下,分布式数据融合因没有剔除严重偏离真实值的传感器估计值,从而影响下一步的融合估计．对此,利用概率数据互联的思想,设计以融合中心预测值为中心、传感器节点估计值为观测值的预测域,并引入定向概率数据互联,对进入预测域的传感器估计值分配权重．仿真结果表明,利用概率数据互联思想的多传感器有效地实现了数据融合,其融合精度较传统分布式融合有所提高;在异常数据明显的情况下,算法的效果更加显著．     

一种多传感器数据自适应空间分级融合算法

当采用分布在不同空间位置上的多传感器观测值对测量噪声干扰下的参数进行融合估计时,被测量的空间分散性对融合结果影响较大.针对该问题,以自适应加权融合算法为基础,提出了自适应空间分级融合算法,并给出了误差分析和应用方法.该算法将融合过程分解为两次寻优,第1次是局部空间的自适应加权寻优,第2次是在全局空间内的融合寻优.计算机仿真结果表明:该算法在估计空间分布不均匀的被测量时优于自适应加权融合算法.     

Target identification based on the transferable belief model interpretation of dempster-shafer model

This paper explains how multisensor data fusion and target identification can be performed within the transferable belief model (TBM), a model for the representation of quantified uncertainty based on belief functions. We present the underlying theory, in particular the general Bayesian theorem needed to transform likelihoods into beliefs and the pignistic transformation needed to build the probability measure required for decision making. We present how this method applies in practice. We compare its solution with the classical one, illustrating it with an embarrassing example, where the TBM and the probability solutions completely disagree. Computational efficiency of the belief-function solution was supposedly proved in a study that we reproduce and we show that in fact the opposite conclusions hold. The results presented here can be extended directly to many problems of data fusion and diagnosis.     

多传感器数据融合系统中的目标跟踪技术

概述了多传感器数据融合系统中目标跟踪的主要技术,分析比较了最近邻法、联合概率法进行数据互联的原理、性能及优缺点,讨论了滤波和Kalman滤波的性能及适用的目标运动模型,并分别给出了用两种互联方法、两种滤波方法进行计算机仿真时的误差比较。实验结果表明,用多传感器数据融合系统进行目标跟踪可以得到较高的跟踪精度。     

多传感器数据融合技术与专家系统研究

简要分析多传感器数据融合技术的理论及研究方法。介绍基于规则的专家系统法在数据融合中的应用;分析专家系统的功能和结构,产生式规则的专家系统的知识表示;介绍正向推理机的设计等。     

Motion estimation of 3-D objects using multisensor data fusion

This article presents an approach to estimate the general 3-D motion of a polyhedral object using multiple sensor data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. The instantaneous state of an object is specified by the rotation, which is defined by a rotation axis and rotation angle, and the displacement of the center of rotation. We have introduced a method based on Moore-Penrose pseudoinverse theory to estimate the instantaneous state of an object, and a linear feedback estimation algorithm to approach the motion estimation. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision. With the assumption that the motion is smooth, our approach can handle the data sequences from multiple sensors with different sampling times. We can also predict the next immediate object position and its motion. The simulation results show our proposed approach is advantageous in terms of accuracy, speed, and versatility.     

Mobile robot localization by fuzzy logic fusion of multisensor data

Interest has been growing in the use of different sensors to increase the capabilities of navigation systems of autonomous vehicles. This area has been studied by several researchers. Nevertheless, this problem has not been solved in a fully satisfying manner. We proposed the method of global, long-term position estimation in a known environment. The method does not require any a priori position prediction or estimation. It is especially suitable in the case of using very simple sensors. The algorithm is based on fuzzy logic fusion of data received from several sensors for some time period. We discuss the methodology of solution evaluation and present some examples of typical criteria. Results of first computer simulation are presented.     

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modeling and processing the uncertain information regardless of prior probabilities, Dempster–Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new belief divergence measure of evidences and the belief entropy was proposed. First, a new Belief Jensen–Shannon divergence is devised to measure the discrepancy and conflict degree between the evidences; then, the credibility degree can be obtained to represent the reliability of the evidences. Next, considering the uncertainties of the evidences, the information volume of the evidences are measured by making use of the belief entropy to indicate the relative importance of the evidences. Afterwards, the credibility degree of each evidence is modified by taking advantage of the quantitative information volume which will be utilized to obtain an appropriate weight in terms of each evidence. Ultimately, the final weights of the evidences are applied to adjust the bodies of the evidences before using the Dempster’s combination rule. A numerical example is illustrated that the proposed method is feasible and effective in handling the conflicting evidences, where the belief value of target increases to 99.05%. Furthermore, an application in fault diagnosis is given to demonstrate the validity of the proposed method. The results show that the proposed method outperforms other related methods where the basic belief assignment (BBA) of the true target is 89.73%.     

Approximating probability distribution of circuit performance function for parametric yield estimation using transferable belief model

This paper applies the transferable belief model （TBM） interpretation of the Dempster-Shafer theory of evidence to approximate distribution of circuit performance function for parametric yield estimation. Treating input parameters of performance function as credal variables defined on a continuous frame of real numbers, the suggested approach constructs a random set-type evidence for these parameters. The corresponding random set of the function output is obtained by extension principle of random set. Within the TBM framework, the random set of the function output in the credal state can be transformed to a pignistic state where it is represented by the pignistic cumulative distribution. As an approximation to the actual cumulative distribution, it can be used to estimate yield according to circuit response specifications. The advantage of the proposed method over Monte Carlo （MC） methods lies in its ability to implement just once simulation process to obtain an available approximate value of yield which has a deterministic estimation error. Given the same error, the new method needs less number of calculations than MC methods. A track circuit of high-speed railway and a numerical eight-dimensional quadratic function examples are included to demonstrate the efficiency of this technique.     

Cognitively-engineered multisensor image fusion for military applications

The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory image information have been and are currently being studied extensively. However, the cognitive aspects of multisensor image fusion have not received as much attention. In this study, a concurrent protocol procedure was used to identify how humans fuse information from visible and infrared imagery in low- and high-stress situations. The results of the concurrent protocol were used to develop operator function models, which were then used to develop preliminary design points for fusing multisensor image data. Fused image data were then used in a combat/target identification simulation, and operator performance, accuracy, and speed were compared with results obtained using unfused data. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors in this particular scenario, and that the algorithm design points show promise for assisting fighter pilots in quicker and more accurate target identification.     

COM-based software architecture for multisensor fusion system

In this paper a framework for constructing flexible, robust and efficient software applications for multisensor fusion system (MFS) is described. Three-tier architecture is exploited so that the whole software system can be divided into three parts: man/machine interface, logic part and database. Design of logic part according to requirements of MFS is emphasized in this paper by using component object model (COM). The result is a COM-based software architecture, which consists of four levels from bottom to top: sensor driver level (SDL), logical sensor level (LSL), fusion unit level (FUL) and task unit level (TUL). Each level is composed of some components with different functions. An intelligent robot system has been designed and developed in our lab based on the idea of the software frame presented above, and explicit advantages are shown extensively.     

无线传感器网络的数据融合研究

利用最优的融合簇状态估计的Krein空间卡尔曼滤波方法,得到信息形式的鲁棒卡尔曼滤波.簇头节点通过所处簇的观测模型,利用信息形式的鲁棒卡尔曼滤波实现离散形式的卡尔曼滤波.簇头节点将状态估计和可逆的误差协方差矩阵传送到中心基站,中心基站融合簇状态估计产生全局状况估计.仿真结果表明,全局状态估计相对于集中状态估计(不分簇),具有更好的性能,且通信代价更低、节点寿命长.     

A process distribution approach for multisensor data fusion systems based on geographical dataspace partitioning

In this work, we present a new approach to distributed sensor data fusion (SDF) systems in multitarget tracking, called TSDF (Tessellated SDF), centered around a geographical partitioning (tessellation) of the data. A functional decomposition divides SDF into components that can be assigned to processing units, parallelizing the processing. The tessellation implicitly defines the set of tracks potentially yielding correlations with the sensor plots (observations) in a tile. Some tracks may occur as correlation candidates for multiple tiles. Conflicts caused by correlations of such tracks with plots in different tiles, are resolved by combining all involved tracks and plots into independent data association problems. The benefit of the TSDF approach to a clustering-based process distribution is independence of the problem space, which yields better scalability and manageability characteristics. The TSDF approach allows scaling in more than one way. It allows SDF for single sensor, multiple sensors on a single platform, and even for multiple sensors on multiple platforms. It also provides the flexibility to scale the processing to the size of the problem. This enables a better control of the throughput, to meet various timing constraints.     

Refined modeling of sensor reliability in the belief function framework using contextual discounting

In belief functions theory, the discounting operation allows to combine information provided by a source in the form of a belief function with meta-knowledge regarding the reliability of that source, resulting in a “weakened”, less informative belief function. In this article, an extension of the discounting operation is proposed, allowing to use more detailed information regarding the reliability of the source in different contexts, i.e., conditionally on different hypotheses regarding the variable on interest. This results in a contextual discounting operation parameterized with a discount rate vector. Some properties of this contextual discounting operation are studied, and its relationship with classical discounting is explained. A method for learning the discount rates is also presented.