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Sensor fusion

About: Sensor fusion is a(n) research topic. Over the lifetime, 26343 publication(s) have been published within this topic receiving 424227 citation(s). The topic is also known as: multi-sensor data fusion.

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Papers
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Open accessBook
01 Aug 1999-
Abstract: The Basics of Target Tracking. Sensor and Source Characteristics. Kinematic State Estimation: Filtering and Prediction. Modelling and Tracking Dynamic Targets. Passive Sensor Tracking. Basic Methods for Data Association. Advanced Methods for MTT Data Association. Attribute Data Fusion. Multiple Sensor Tracking -- Issues and Methods. Multiple Sensor Tracking -- System Implementation and Applications. Reasoning Schemes for Situation Assessment and Sensor Management. Situation Assessment. Tracking System Performance Prediction, and Evaluation. Multi Target Tracking with an Agile Beam Radar. Sensor Management. Multiple Hypothesis Tracking System Design and Application. Detection and Tracking of Dim Targets in Clutter.

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Topics: Tracking system (62%), Track-before-detect (55%), Sensor fusion (55%) ...read more

2,773 Citations


Journal ArticleDOI: 10.1109/5.554205
David L. Hall1, James LlinasInstitutions (1)
01 Jan 1997-
Abstract: Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

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Topics: Sensor fusion (58%)

2,218 Citations


Open accessBook
05 Dec 1996-
Abstract: 1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography.

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1,730 Citations


Journal ArticleDOI: 10.1016/J.INFFUS.2011.08.001
01 Jan 2013-Information Fusion
Abstract: There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.

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1,455 Citations


Proceedings ArticleDOI: 10.1109/SNPA.2003.1203354
11 May 2003-
Abstract: This paper presents and analyzes an architecture to collect sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. MULEs pick up data from the sensors when in close range, buffer it, and drop off the data to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points) and the required buffer capacities on the sensors and the MULEs. The modeling along with simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.

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Topics: Wireless sensor network (55%), Sensor fusion (52%)

1,449 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202224
20211,075
20201,280
20191,498
20181,353
20171,218

Top Attributes

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Topic's top 5 most impactful authors

Pramod K. Varshney

117 papers, 8.1K citations

Erik Blasch

76 papers, 2K citations

Hugh Durrant-Whyte

57 papers, 3.3K citations

Yaakov Bar-Shalom

30 papers, 2.4K citations

Klaus Dietmayer

27 papers, 813 citations

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