Stable Performance Under Sensor Failure of Local Positioning Systems

Díez-González, Javier; Álvarez, Rubén; Verde, Paula; Ferrero-Guillén, Rubén; Bárcena, David González; Pérez, Hilde

doi:10.1007/978-3-030-57802-2_48

Cited by 2 publications

(1 citation statement)

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“…This covariance matrix must be modeled according to the system uncertainties: noise in Line-of-Sight (LOS) and NLOS environments [18] and clock errors [6]. In the TDOA architecture, the time measurements are correlated [20,21], therefore a complete characterization of the covariance matrix must be accomplished:…”

Section: Cramèr-rao Bound For the Tdoa Architecturementioning

confidence: 99%

Node Distribution Optimization in Positioning Sensor Networks through Memetic Algorithms in Urban Scenarios

Verde

Ferrero-Guillén

Álvarez

et al. 2020

7th International Electronic Conference on Sensors and Applications

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Local Positioning Systems rely on ad-hoc node deployments which fit the environment characteristics in order to reduce the system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem. This problem has been assigned as NP-Hard, therefore a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight environments in which there is not continuity in the fitness function evaluation among contiguous solutions, challenges arise for the GA. Consequently, in this paper, we introduce a Memetic Algorithm (MA) with a Local Search strategy for exploring the most different individuals of the population in search of improving the NLP results in urban scenarios for the first time. Results show that the MA proposed outperforms the GA optimization and attains an improvement of 6.51% in accuracy in the scenario proposed.

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