Optimal m-ary data fusion with distributed sensors

Baek, Wonjang; Bommareddy, Susilpa

doi:10.1109/7.395226

Cited by 29 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When M-ary hypothesis testing was considered, the local detectors (LDs) were often assumed to transmit at least log 2 M bits to the Data Fusion Center (DFC) for every observation [1,12]. However, the cardinality of the local decisions need not be equal to the number of hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Distributed M-ary hypothesis testing with binary local decisions

Zhu¹,

Yuan

Rorres

et al. 2004

Information Fusion

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Distributed M-ary hypothesis testing with binary local decisions

Zhu¹,

Yuan

Rorres

et al. 2004

Information Fusion

View full text Add to dashboard Cite

“…During the simulations, Rician K factor was K = 12 [28]. In all these plots, the legend "PAC" represents the simulation results of the optimal parallel fusion rule [29] in an ideal radio channel without any noise, where all the original local decisions from the sensors are available at the DFC to formulate the fusion rule. Therefore, the detection performance of the optimal "PAC" fusion rule is fairly comparable to any MAC scheme based on MIMO spatial multiplexing in a noiseless radio channel.…”

Section: A Simulation Assumptionsmentioning

confidence: 99%

Data Fusion in the Air With Non-Identical Wireless Sensors

Panigrahi

Björsell

Bengtsson

2019

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

In this paper, a multi-hypothesis distributed detection technique with non-identical local detectors is investigated. Here, for a global event, some of the sensors/detectors can observe the whole set of hypotheses, whereas the remaining sensors can either see only some aspects of the global event or infer more than one hypothesis as a single hypothesis. Another possible option is that different sensors provide complementary information. The local decisions are sent over a multiple access radio channel so that the data fusion is formed in the air before reaching the decision fusion center (DFC). An optimal energy fusion rule is formulated by considering the radio channel effects and the reliability of the sensors together, and a closed-form solution is derived. A receive beamforming algorithm, based on a modification of Lozano's algorithm, is proposed to equalize the channel gains from different sensors. Sensors with limited detection capabilities are found to boost the overall system performance when they are used along with fully capable sensors. The additional transmit power used by these sensors is compensated by the designed fusion rule and the antenna array gain. Additionally, the DFC, equipped with a large antenna array, can reduce the overall transmit energy consumption without sacrificing the detection performance.

show abstract

“…For the Bayes fusion rule [1,2], the user specifies the costs of a false alarm C F , a missed detection C M and (for M > 2) a mix-up between two threat possibilities C X . For each combination of individual sensor decisions {S n }, the system finds a fused decision F that minimizes the Bayes risk, or the expected cost of a wrong decision,…”

Section: Static Fusion Using Bayesian Networkmentioning

confidence: 99%

Sequential testing over multiple stages and performance analysis of data fusion

Thakur

2013

SPIE Proceedings

View full text Add to dashboard Cite

We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structures to be stacked and analyzed efficiently. We then present an extension of the Wald sequential test for combining the outputs of the Bayesian network over time. We discuss an algorithm to compute its performance statistics and illustrate the approach on some examples. This variant of the sequential test involves multiple, distinct stages, where the evidence accumulated from each stage is carried over into the next one, and is motivated by a need to keep certain sensors in the network inactive unless triggered by other sensors.Acknowledgement.

show abstract

Optimal m-ary data fusion with distributed sensors

Cited by 29 publications

References 8 publications

Distributed M-ary hypothesis testing with binary local decisions

Distributed M-ary hypothesis testing with binary local decisions

Data Fusion in the Air With Non-Identical Wireless Sensors

Sequential testing over multiple stages and performance analysis of data fusion

Contact Info

Product

Resources

About