Stable Performance Under Sensor Failure of Local Positioning Systems
- Javier Díez-González 1
- Rubén Álvarez 1
- Paula Verde 1
- Rubén Ferrero-Guillén 1
- David González-Bárcena 2
- Hilde Pérez 1
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1
Universidad de León
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2
Universidad Politécnica de Madrid
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- Álvaro Herrero (coord.)
- Carlos Cambra (coord.)
- Daniel Urda (coord.)
- Javier Sedano (coord.)
- Héctor Quintián (coord.)
- Emilio Corchado (coord.)
Publisher: Springer Suiza
ISBN: 978-3-030-57801-5, 978-3-030-57802-2
Year of publication: 2021
Pages: 499-508
Congress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)
Type: Conference paper
Abstract
Local Positioning Systems are an active topic of research in the field of autonomous navigation. Its application in difficult complex scenarios has meant a solution to provide stability and accuracy for high-demanded applications. In this paper, we propose a methodology to enhance Local Positioning Systems performance in sensor failure contexts. This fact guarantees system availability in adverse conditions. For this purpose, we apply a Genetic Algorithm Optimization in a five-sensor 3D TDOA architecture in order to optimize the sensor deployment in nominal and adverse operating conditions. We look for a trade-off between accuracy and algorithm convergence in the position determination in four (failure conditions) and five sensor distributions. Results show that the optimization with failure consideration outperforms the non-failure optimization in a 47% in accuracy and triples the convergence radius size in failure conditions, with a penalty of only 6% in accuracy during normal performance.