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The accuracy of a system is measured by the deviation of the system's results from the actual results. Information fusion deals with the combination of information from same source or different sources to obtain improved fused estimate with greater quality or greater relevance. As larger amount of sensors are deployed in harsher environment, it is important that sensor fusion techniques are robust...
In this paper we examine near-optimal SLAM exploration in Gaussian processes. We propose a submodular sensing quality function that extends studies from discrete sensor placement to an autonomous sampling scheme where sensing sites must be visited frequently. This is beneficial in the SLAM context, where sensing sites themselves bear uncertainties. Also in time-critical applications, we have to balance...
Aiming at the conflict circumstances of multi-sensor information system, the paper introduces the idea of game theory into data fusion and introduce a method to filter the conflict data based on mutual entropy to improve the reliability and accuracy of the fusion result. A function model referring to bus structure of JDL (Joint Directors of Laboratories) model is established and fusion algorithm based...
The development and use of many diverse ontologies to support the representational needs of different sources and different contexts is common and necessary. However, the increased sharing of databases implementing heterogeneous ontologies pose the problem of ontological alignment. Ontology alignment typically consists of manual operations from users with different experiences and understandings and...
Based on the evidence theory, combing with gray correlation and information entropy theory, a new method is proposed for machinery fault diagnosis. Firstly, based on information entropy feature of machinery fault, it builds the standard feature vectors of fault diagnosis. Secondly, the Basic Probability Assignment Function (BPAF) of evidence is built by gray correlation theory, and then a space-time...
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain modeling for a mobile robot. Building a model of large scale and complex terrain that can adequately handle uncertainty and incompleteness in a statistically sound manner is a very challenging problem. To obtain a comprehensive...
This paper is concerned with probabilistic evaluation of multiple-frame data association hypotheses in multiple-target tracking problems, in particular, when targets are not necessarily independent a priori. Multiple-target tracking problems with dependent targets naturally arise whenever targets interact with each other, as they move in congested traffic, or as they actively coordinate their movements...
Sensor fusion is the notion of combining the data from two or more sensors in order to obtain enhanced performance compared with that of the individual sensors. In addition, Signal Detection Theory can be used to monitor how well a sensor operates. That is, through the number of hits, misses, false alarms and correct rejections a sensor registers, we gain a better understanding as to how reliably...
D-S evidence theory is an effective tool to deal with uncertain information fusion, but there is a disadvantage in dealing with high confliction between evidences. Considering different evidences have different reliabilities, this paper, which is based on the framework of random set, proposed a concept of relative information entropy to acquire the reliability weight of evidence itself and evidence...
To task space-based sensors to efficiently estimate the states of targets, an information theoretic approach is developed based on Kullback-Leibler (KL) discrimination for myopic sensor resource allocation. The technique employs the principle that sensors should take actions that maximize the expected KL discrimination as information gain. Calculate KL discrimination between the priori state probability...
We present arguments that a small number of sensors within the network provide most of the utility. That is, cooperation of more than a small number of nodes has little benefit. We present two scenarios. In the first scenario, all sensors provide identical utility, and their utilities are aggregated sequentially. The second scenario is sensor fusion with signal strength decreasing with distance. In...
Data fusion within the evidential reasoning framework is a well established, robust and conservative technique to fuse uncertain information from multiple sensors. A number of fusion methods within this formalism were introduced including Dempster-Shafer theory (DST) fusion, Dezert Samarandche fusion (DSmT), and Smets' transferable belief model (TBM) based fusion. However, the impact of fusion on...
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