Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization

Domingo-Perez, Francisco; Galilea, José Luis Lázaro; Wieser, Andreas; Martín-Gorostiza, Ernesto; Salido-Monzú, David; Llana, Álvaro de la

doi:10.1016/j.eswa.2015.11.008

Cited by 60 publications

(39 citation statements)

References 21 publications

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“…In order for the multilateration technique to work properly, the stations used for measurements have to be placed at strategic locations to avoid an ill-conditioned system of equations; this is typically known as a sensor placement problem for target localization. In case one can control x/y/z of each station, the location of each station can be optimized to improve the accuracy of the solution (Mantilla Gaviria et al 2013;Domingo-Perez et al 2016). Benazzouz et al (2015) presented a multilateration technique to solve jointly for the OBS position and the water layer average velocity.…”

Section: Introductionmentioning

confidence: 99%

Accurate Ocean Bottom Seismometer Positioning Method Inspired by Multilateration Technique

et al. 2018

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The positioning of ocean bottom seismometers (OBS) is a key step in the processing flow of OBS data, especially in the case of self popup types of OBS instruments. The use of first arrivals from airgun shots, rather than relying on the acoustic transponders mounted in the OBS, is becoming a trend and generally leads to more accurate positioning due to the statistics from a large number of shots. In this paper, a linearization of the OBS positioning problem via the multilateration technique is discussed. The discussed linear solution solves jointly for the average water layer velocity and the OBS position using only shot locations and first arrival times as input data.

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Section: Introductionmentioning

confidence: 99%

Accurate Ocean Bottom Seismometer Positioning Method Inspired by Multilateration Technique

et al. 2018

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“…While the practicality of a system is the key to its widespread adoption in industry, the practical aspects of tracking systems applying to construction have not been adequately addressed, perhaps because such application involve a number of factors beyond accuracy and cost, including deployment, system coordination, system management, and form factor, all of which are thoroughly reviewed in an article by Li et al (2016a). Recent research in various domains including electrical engineering and computer science explored a number of theoretical approaches for sensor deployment, such as multi-objective optimization (Domingo-Perez et al 2016), convex optimization with estimation theory (Moreno-Salinas et al 2013), signal energy loss (Cho et al 2018) and the Fisher information matrix-based optimization (Nguyen and Dogancay 2015). These studies present advanced mathematical algorithmic approaches to solving the complex phenomena between signals and the environment and demonstrate the performance through computer simulation.…”

Section: Introductionmentioning

confidence: 99%

Automated and Optimized Sensor Deployment using Building Models and Electromagnetic Simulation

2018

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With the advent of wireless sensing technology and interest in tracking resources, researchers have developed advanced tracking algorithms by using one or more sensor systems for improved accuracy and reliability of tracking. The objective of this research lies in another aspectdeployment-of tracking that has received only little attention until now. The research explores a method for sensor deployment particularly designed for the building in which the sensors are used. To tailor our solution to a specific building, we integrate a building information model with an electromagnetic energy analysis. By using such a model, the system extracts the properties of building materials, which are used as parameters of sensor deployment optimization. Then, we find a method of optimizing the deployment of a received signal strength indication (RSSI)-based

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“…The topic of multi-objective optimizations is huge because it could be applied in so many applications in various fields including economics, finance, optimal control, process optimization, optimal design, etc. In the field of optimal design alone, diverse applications exist like nano-CMOS voltage-controlled oscillator design [15], antenna design [16], optimal sensor deployment [17], etc., while it hasn't been considered in the design of EMATs. We developed the optimization programs in Matlab, and achieved performance enhancement by decreasing the total number of evaluations of the objective functions.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective structural optimization of an omnidirectional electromagnetic acoustic transducer

et al. 2017

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In this paper an axisymmetric model of an omnidirectional electromagnetic acoustic transducer (EMAT) used to generate Lamb waves in conductive plates is introduced. Based on the EMAT model, the structural parameters of the permanent magnet were used as the design variables while other parameters were fixed. The goal of the optimization was to strengthen the generation of the A0 mode and suppress the generation of the S0 mode. The amplitudes of the displacement components at the observation point of the plate were used for calculation of the objective functions. Three approaches to obtain the amplitudes were discussed. The first approach was solving the peak values of the envelopes of the time waveforms from the time domain simulations. The second approach also involved calculation of the peaks, but the waveforms were from frequency domain model combined with the forward and inverse Fourier transforms. The third approach involved a single frequency in the frequency domain model. Single and multi-objective optimizations were attempted, implemented with the genetic algorithms. In the single objective optimizations, the goal was decreasing the ratio of the amplitudes of the S0 and A0 modes, while in the multi-objective optimizations, an extra goal was strengthening the A0 mode directly. The Pareto front from the multi-objective optimizations was compared with the estimation from the data on the discrete grid of the design variables. From the analysis of the results, it could be concluded that for a linearized steel plate with a thickness of 10mm and testing frequency of 50kHz, the point with minimum S0/A0 could be selected, thus the multi-objective optimization effectively degenerated to the single objective optimization. While for an aluminum plate with a thickness of 3mm and frequency of 150kHz, without further information it would be difficult to select one particular solution from the Pareto front.

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Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization

Cited by 60 publications

References 21 publications

Accurate Ocean Bottom Seismometer Positioning Method Inspired by Multilateration Technique

Accurate Ocean Bottom Seismometer Positioning Method Inspired by Multilateration Technique

Automated and Optimized Sensor Deployment using Building Models and Electromagnetic Simulation

A multi-objective structural optimization of an omnidirectional electromagnetic acoustic transducer

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