AOA estimation of two narrowband signals using interferometry

Ly, Peter Q. C.; Elton, Stephen D.; Gray, Douglas A.

doi:10.1109/array.2010.5613238

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The LPN from P i,N (x,y,z) provides the five-tuple P i ,N,M (x,y,x,l,p), where l is the likelihood of each known place p to contain the nonbeacon node i. The subscript M represents the different possibilities for each non-beacon node i under same NLA N. For example, P 20,4,1 (1,2,3,70,1) and P 20,4,2 (2,4,6,30,6), i.e., through the NLA number 4 the first estimated position of the non-beacon node 20 is (1,2,3) with 70% likelihood of being in the place number 1 and the second one is (2,4,6) with 30% likelihood of being in the place number 6. In (iv), the Evaluator selects the estimated position with the highest likelihood for each non-beacon node.…”

Section: A Architecturementioning

confidence: 99%

“…Some range-based NLAs are characterized by their high accuracy; they usually handle errors of a few centimeters. For instance, the Angle of Arrival (AoA) [20] [21], the Time of Arrival (ToA) [22][3] and the Time Difference of Arrival (TDoA) [23] [24]. NLAs based on the TDoA require for each node one speaker and one microphone.…”

Section: B Range-based Localization Algrithmsmentioning

confidence: 99%

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Smart Environmental Architecture for Node Localization in a Wireless Sensor Network

Jauregui-Ortiz

Siller

Ramos

et al. 2012

2012 Eighth International Conference on Intelligent Environments

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As far as we know, there is not a Node Localization Algorithm (NLA) that presents the same accuracy for all possible scenarios. We believe that a NLA should be able to "interpret" the dynamic information of the environment. In this sense, simple NLAs are rather focused and might perform well for specific scenarios and applications. Therefore, information fusion and context awareness seems to be an appropriate approach to address this issue. We propose the Smart Environmental Architecture for Node Localization (SEA-NL), which is composed by two main elements: (i) the Smart Beacon Nodes (SBNs) and (ii) the Logical Position of Nodes (LPN). In (i) the obstruction level indicator is estimated and can improve the estimation of distances among nodes. In (ii) environment information and a one to one relation between a node and an object are used andcan also improve location estimation. Via simulation, our architecture was tested indoors and outdoors considering three localization algorithms: the Weighed Centroid Localization (WCL), the Centroid Localization, and the Triangular Centroid Localization. Finally, we present an accuracy comparison among NLAs used in isolated way, and by using the SBNs, the LPN, and the SEA-NL, where our architecture improves WCL up to ~30.88%.

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