Open AccessProceedings Article
Sensor selection for active information fusion
Yongmian Zhang,Qiang Ji +1 more
- pp 1229-1234
TLDR
This paper presents a methodology to actively select a sensor subset with the best tradeoff between information gain and sensor cost by exploiting the synergy among sensors.Abstract:
Active information fusion is to selectively choose the sensors so that the information gain can compensate the cost spent in information gathering. However, determining the most informative and cost-effective sensors requires an evaluation of all possible sensor combinations, which is computationally intractable, particularly, when information-theoretic criterion is used. This paper presents a methodology to actively select a sensor subset with the best tradeoff between information gain and sensor cost by exploiting the synergy among sensors. Our approach includes two aspects: a method for efficient mutual information computation and a graph-theoretic approach to reduce search space. The approach can reduce the time complexity significantly in searching for a near optimal sensor subset.read more
Citations
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Journal ArticleDOI
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
Yongmian Zhang,Qiang Ji +1 more
TL;DR: An information fusion framework based on dynamic Bayesian networks is proposed to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources.
Journal ArticleDOI
Toward a decision-theoretic framework for affect recognition and user assistance
TL;DR: A general unified decision-theoretic framework based on influence diagrams for simultaneously modeling user affect recognition and assistance is presented and a non-invasive real-time prototype system to recognize different user affective states from four-modality user measurements is built.
Journal ArticleDOI
Efficient Sensor Selection for Active Information Fusion
Yongmian Zhang,Qiang Ji +1 more
TL;DR: This paper introduces an alternative measure to multisensor mutual information for characterizing the sensor information gain and proposes an approximated nonmyopic sensor selection method that can efficiently and near-optimally select a subset of sensors for active fusion.
Proceedings ArticleDOI
COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes
TL;DR: An optimization problem to select set of cameras and inference dependencies for each person which attempts to minimize the computational cost under given performance constraints is presented and results show the efficiency of COST in improving the performance of such systems and reducing the computational resources required.
Proceedings ArticleDOI
A multi-UAV targeting algorithm for ensemble forecast improvement
Han-Lim Choi,Jonathan P. How +1 more
TL;DR: An algorithm for targeting a team of UAVs as sensor platforms to ensure the improvement in the forecast at a separate verification time and location within the ensemble forecast framework is presented.
References
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Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book
Decisions with Multiple Objectives: Preferences and Value Trade-Offs
TL;DR: In this article, a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
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Multisensor Data Fusion
TL;DR: This new edition is now in two volumes and contains nine new chapters and focuses on the most recent developments in the fusion of data in a variety of applications from military to automotive to medical.
Book ChapterDOI
Multisensor data fusion
TL;DR: The idea is that fusion of complementary information available from different sensors will yield more accurate results for information processing problems and perfect emulation of the human brain remains an elusive goal.