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How to quantify the uncertainty information consisted in the body of evidence (BOE) in the framework of Dempster-Shafer evidence theory is still an open issue. A few uncertainty measures have been proposed in Dempster-Shafer evidence theory framework, but these studies mainly focused on the mass function itself and the scale of the frame of discernment (FOD) is totally ignored. Since the existing...
This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these...
Pulsars autonomous navigation is characterized by its credible performance and high navigation accuracy, which will have broad prospects for use in deep-space autonomous navigation. In our study, we find the observation time required for different pulsars to obtain the same time of arrival (TOA) accuracy is different, which means the rates of different sensors are different. However, the previous...
This paper considers the sensor selection problem for target tracking in large-scale sensor networks. We propose a new sensor selection strategy based on dual-criterion optimization. Both the bias change detection and information gain maximization are considered as criteria in our proposed sensor selection strategy. This new approach extends the sensor selection problem from single criterion optimization...
Advanced driver assistance systems rely on the availability of robust information on the driving situation and the driver's needs and intentions to operate reasonably and safely. For this, they have to be enabled to identify and assess both the driving situation and the driver's intentions on the basis of features that can be measured by the vehicle. In case of the prediction of lane change maneuvers...
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments...
Defect localization in Very Large Integration Circuits (VLSI) requires to use multi-sensor information such as electrical waveforms, emission microscopy images and frequency mapping in order to detect, localize and identify the failure. Each sensor provides a specific kind of feature modeling the evidence. Thus, the defect localization in VLSI can be summarized as a problem of data fusion with heterogeneous...
Decentralized data fusion is a challenging task. Either it is too difficult to maintain and track the information required to perform fusion optimally, or too much information is discarded to obtain informative fusion results. A well-known solution is Covariance Intersection, which may provide too conservative fusion results. A less conservative alternative is discussed in this paper, and generalizations...
Multi-sensor fusion has been extensively studied i information fusion field, and the distributed target detection i one of the most important applications in the multiple sensor detection theories. In this paper, a data fusion algorithm for target detection is proposed based on tree topology combine with the orderly full binary tree and we discuss the optima threshold fusion rule problem. Different...
This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the...
Situational Awareness (SAW) is a widespread concept in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. A poor SAW can induce humans to failures in the decision-making process, leading to losses of lives and property damage. Data fusion processes present opportunities to enrich the knowledge about situations by integrating...
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