A Review of Data Fusion Techniques
TL;DR: This paper summarizes the state of the data fusion field and describes the most relevant studies, enumerate and explain different classification schemes for data fusion, and reviews the most common algorithms.
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Abstract: The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.
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References
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Application of 0-1 integer programming to multitarget tracking problems
TL;DR: This paper presents a new approach to the solution of multi-target tracking problems that is approached as an unsupervised pattern recognition problem and has the computational structure of the set packing and set partitioning problems of 0-1 integer programming.
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Distributed particle filters for sensor networks
Mark Coates
- 26 Apr 2004
TL;DR: Two methodologies for performing distributed particle filtering in a sensor network by adding a predictive scalar quantizer training step into the more standard particle filtering framework, allowing adaptive encoding of the measurements.
Sensor Fusion Using Dempster-Shafer Theory
Huadong Wu,Mel Siegel,Rainer Stiefelhagen,Jie Yang +3 more
- 01 Jan 2002
TL;DR: The relationship between Dempster-Shafer theory and the classical Bayesian method is discussed and the experimental approach is to track a user’s focus of attention from multiple cues.
Data Fusion Lexicon
Franklin E. White
- 01 Oct 1991
TL;DR: The purpose of this lexicon is to provide some common terminology for theoreticians, developers and users involved in the field of data fusion to help facilitate the exchange of information and cooperation within the data fusion community through the enhancement of communications and understanding.