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A toxonomy of multi-sensor fusion
Journal of Manufacturing Systems, 1992Abstract This paper develops a taxonomy for multi-sensor fusion and creates a general formulation to describe the process. Using this formulation as a guide, we identify and discuss the following classes of data for sensor fusion: uniquely determined, over determined, under determined, and sequential data.
Ralph Tanner, Nan K. Loh
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2014
In the previous chapters, we have discussed issues concerning hardware, communication and network topologies for the practical deployment of Body Sensor Networks (BSNs). The pursuit of low power miniaturised distributed sensing under a patient’s natural physiological conditions has also imposed significant technical challenges on integrating ...
Guang-Zhong Yang +3 more
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In the previous chapters, we have discussed issues concerning hardware, communication and network topologies for the practical deployment of Body Sensor Networks (BSNs). The pursuit of low power miniaturised distributed sensing under a patient’s natural physiological conditions has also imposed significant technical challenges on integrating ...
Guang-Zhong Yang +3 more
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Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480), 2003
This paper presents a new approach to solve the sensor fusion problem. This new approach is called multi-sensor/knowledge fusion (MSKF) which takes into account not only the fusion of information between multiple sensory systems but also the fusion between sensory information with system's prior knowledge of its environment. This MSKF architecture is a
C. Hiransoog, C.A. Malcolm
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This paper presents a new approach to solve the sensor fusion problem. This new approach is called multi-sensor/knowledge fusion (MSKF) which takes into account not only the fusion of information between multiple sensory systems but also the fusion between sensory information with system's prior knowledge of its environment. This MSKF architecture is a
C. Hiransoog, C.A. Malcolm
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Multi-sensor fusion with Bayesian inference
1997This paper describes the development of a Bayesian framework for multiple graph matching. The study is motivated by the plethora of multi-sensor fusion problems which can be abstracted as multiple graph matching tasks. The study uses as its starting point the Bayesian consistency measure recently developed by Wilson and Hancock.
Mark L. Williams 0002 +2 more
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Distributed multi-sensor fusion
SPIE Proceedings, 2008McQ has developed a broad based capability to fuse information in a geographic area from multiple sensors to build a better understanding of the situation. The paper will discuss the fusion architecture implemented by McQ to use many sensors and share their information.
Peter Scheffel +3 more
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Multi-sensor image fusion with SCDPT transform
2013 15th IEEE International Conference on Communication Technology, 2013A multi-sensor image fusion algorithm based on SCDPT transform is proposed in this paper. SCDPT transform is used to decompose source images in each scale and direction to get low-pass sub-band coefficients and band-pass directional sub-band coefficients.
Qian Hu +4 more
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System identification for multi-sensor data fusion
2015 American Control Conference (ACC), 2015In this paper we discuss the problem of combining sensor information for two main detection problems: 1) two variants of a spatial search problem and 2) a fault detection problem for a three tank system (TTS). In all cases the assumption is that data may be collected from multiple sensors.
Karla Hernandez, James C. Spall
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A Multi-sensor Fusion Framework in 3-D
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013The majority of existing image fusion techniques operate in the 2-d image domain which perform well for imagery of planar regions but fails in presence of any 3-d relief and provides inaccurate alignment of imagery from different sensors. A framework for multi-sensor image fusion in 3-d is proposed in this paper.
Vishal Jain 0003 +2 more
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Deep Transform Learning for Multi-Sensor Fusion
2020 28th European Signal Processing Conference (EUSIPCO), 2021This paper presents a Deep Transform Learning based framework for multi-sensor fusion. Deep representations are learnt for each of the sensors by stacking one transform after another. Subsequently, a common transform is utilized to fuse the deep representations of all sensors to estimate the output.
Saurabh Sahu +3 more
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Recursive track fusion for multi-sensor surveillance
Information Fusion, 2004Abstract Most of the track fusion work documented in the literature involves a post-processing, batch approach to track-to-track association. Also, it is generally assumed that, due to bandwidth constraints, sensor contacts are unavailable to the track fusion system.
Stefano Coraluppi, Craig Carthel
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