Distributed detection with multiple sensors I. Advanced topics

Blum, Rick S.; Kassam, S.A.; Poor, H. Vincent

doi:10.1109/5.554209

Cited by 583 publications

(311 citation statements)

References 58 publications

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“…The goal was to design the radars and the fusion centre to detect an event as accurately as possible, subject to an alphabetsize constraint on the messages transmitted by each sensor node. A survey of early work in this field is provided by Tsitsiklis [4], Viswanathan & Varshney [5], Blum et al [6] and Varshney [7]. There has been renewed interest in distributed detection in the context of modern WSNs (see [8,9] for recent surveys).…”

Section: Detection and Parameter Estimationmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Section: Detection and Parameter Estimationmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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“…For a more complete overview of decentralized detection the reader is recommend to read Varshney, 1997;Signal Processing Magazine, Special Issue , 2006. Unlike in classical decentralized detection problems (Blum, Kassam & Poor, 1997;Varshney, 1997), greater challenges exist in a WSN setting. There are stringent power constraints for each node, and communication channels from nodes to the FC are severely bandwidthconstrained.…”

Section: Fig 4 Example Of Distributed Detection Scenariomentioning

confidence: 99%

Building Context Aware Network of Wireless Sensors Using a Scalable Distributed Estimation Scheme for Real-time Data Manipulation

Basirat¹,

Khan²

2010

Wireless Sensor Networks: Application-Centric Design

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“…The SPRT has been widely used for RADAR target detection [9], [16], [25], [28], and also for pattern recognition [20], [13] and multisensor fusion systems [2], [6], [15], [19] . For notational simplicity we assume we are interested in only one class T = 1 θ .…”

Section: Sprt For Pattern Recognitionmentioning

confidence: 99%

Markov sequential pattern recognition : dependency and the unknown class.

Malone¹,

Haschke²,

Koch³

2004

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The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies.Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.Index Terms: Markov dependence, dependent observations, sequential pattern recognition, sequential probability ratio test, unknown class. 3Intentionally Left Blank 4

show abstract

Distributed detection with multiple sensors I. Advanced topics

Cited by 583 publications

References 58 publications

Distributed inference in wireless sensor networks

Distributed inference in wireless sensor networks

Building Context Aware Network of Wireless Sensors Using a Scalable Distributed Estimation Scheme for Real-time Data Manipulation

Markov sequential pattern recognition : dependency and the unknown class.

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