Detection with Distributed Sensors

Tenney, Robert R.; Sandell, N.

doi:10.1109/taes.1981.309178

Cited by 749 publications

(381 citation statements)

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“…Decentralized detection in sensor networks has attracted a lot of interest in recent years, because of new technologies (especially, the availability of low-cost sensing devices) and numerous potential applications. The decentralized detection problem was first formulated and studied by [3], which considers a "parallel configuration" whereby each sensor makes an observation and sends a quantized version of that observation to a fusion center. The goal is to make a decision on the two possible hypotheses, based on the messages received at the fusion center.…”

Section: Introductionmentioning

confidence: 99%

Error Exponents for Decentralized Detection in Tree Networks

Tay

Tsitsiklis

2008

Networked Sensing Information and Control

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Summary. We consider the problem of decentralized detection in a sensor network consisting of nodes arranged as a tree of bounded height. We characterize the optimal error exponent under a Neyman-Pearson formulation, and show that the Type II error probability decays exponentially with the number of nodes. Surprisingly, the optimal error exponent is often the same as that corresponding to a parallel configuration. We provide sufficient, as well as necessary, conditions for this to happen. We also consider the impact of failure-prone sensors or unreliable communications between sensors on the detection performance. Simple strategies that nearly achieve the asymptotically optimal performance in these cases are also developed.

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Section: Introductionmentioning

confidence: 99%

Error Exponents for Decentralized Detection in Tree Networks

Tay

Tsitsiklis

2008

Networked Sensing Information and Control

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“…Our results for QAM can be viewed as a generalization of classical results on the hard decision penalty for binary communication in [1]- [3], while our results for larger PSK alphabets draw on the relatively recent low-SNR analysis in [4]. Prior work: There is a large body of related literature dating back more than three decades on the broad subjects of "multiterminal inference" [5]- [7], "distributed hypothesis testing" [8]- [10], and "distributed detection" [11]- [15]. In general, the setting in these problems is to have multiple agents forward quantized observations to a fusion center which then applies a detection or estimation rule according to a certain performance objective.…”

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confidence: 99%

Distributed Reception with Hard Decision Exchanges

Brown

Madhow

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-This paper considers the problem of jointly processing messages received over a forward link from a single distant transmitter to a cooperative receive cluster connected by a local area network with finite available throughput. For N cooperating receivers, ideal distributed receive beamforming with direct exchange of unquantized observations leads to an Nfold gain in signal-to-noise ratio (SNR) for equal-gain additive white Gaussian noise channels, with significant additional gains over fading channels due to diversity. It is shown in this paper that a significant portion of these gains can be obtained simply by exchanging hard decisions among some or all of the nodes in the receive cluster. Mutual information computations and simulations of LDPC-coded systems show that optimal combining of hard decisions tends to perform within 0.5-2 dB of ideal receive beamforming. For the low per-node SNR regime of interest with large receive clusters, asymptotic analysis of a suboptimal combining technique termed "pseudo-beamforming" shows that distributed reception with hard decision exchanges performs within 1-2 dB of ideal receive beamforming.

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“…Distributed detection with fusion was an active research field during the 1980s and early 1990s, starting with the seminal work described by Tenney & Sandell [3]. The application driver for this research was distributed radar with a central command (fusion) centre.…”

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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Detection with Distributed Sensors

Cited by 749 publications

References 1 publication

Error Exponents for Decentralized Detection in Tree Networks

Error Exponents for Decentralized Detection in Tree Networks

Distributed Reception with Hard Decision Exchanges

Distributed inference in wireless sensor networks

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