Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises
In source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7205 |
| _version_ | 1797476956833316864 |
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| author | Murillo Villa Bruno Ferreira Nuno Cruz |
| author_facet | Murillo Villa Bruno Ferreira Nuno Cruz |
| author_sort | Murillo Villa |
| collection | DOAJ |
| description | In source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the source, and the criteria used to evaluate the localization performance. This paper addresses the problem of optimum sensor placement in a plane for the localization of an underwater vehicle moving in 3D. We consider sets of sensors that measure the distance to the vehicle and model the measurement noises with distance dependent covariances. We develop a genetic algorithm and analyze both single and multi-objective problems. In the former, we consider as the evaluation metric the arithmetic average along the vehicle trajectory of the maximum eigenvalue of the inverse of the Fisher information matrix. In the latter, we estimate the Pareto front of pairs of common criteria based on the Fisher information matrix and analyze the evolution of the sensor positioning for the different criteria. To validate the algorithm, we initially compare results with a case with a known optimal solution and constant measurement covariances, obtaining deviations from the optimal less than 0.1%. Posterior, we present results for an underwater vehicle performing a lawn-mower maneuver and a spiral descent maneuver. We also present results restricting the allowed positions for the sensors. |
| first_indexed | 2024-03-09T21:12:08Z |
| format | Article |
| id | doaj.art-75a3ecc0b87f49d19f27b1d862e4d664 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:12:08Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-75a3ecc0b87f49d19f27b1d862e4d6642023-11-23T21:45:12ZengMDPI AGSensors1424-82202022-09-012219720510.3390/s22197205Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent NoisesMurillo Villa0Bruno Ferreira1Nuno Cruz2INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 378, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 378, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 378, 4200-465 Porto, PortugalIn source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the source, and the criteria used to evaluate the localization performance. This paper addresses the problem of optimum sensor placement in a plane for the localization of an underwater vehicle moving in 3D. We consider sets of sensors that measure the distance to the vehicle and model the measurement noises with distance dependent covariances. We develop a genetic algorithm and analyze both single and multi-objective problems. In the former, we consider as the evaluation metric the arithmetic average along the vehicle trajectory of the maximum eigenvalue of the inverse of the Fisher information matrix. In the latter, we estimate the Pareto front of pairs of common criteria based on the Fisher information matrix and analyze the evolution of the sensor positioning for the different criteria. To validate the algorithm, we initially compare results with a case with a known optimal solution and constant measurement covariances, obtaining deviations from the optimal less than 0.1%. Posterior, we present results for an underwater vehicle performing a lawn-mower maneuver and a spiral descent maneuver. We also present results restricting the allowed positions for the sensors.https://www.mdpi.com/1424-8220/22/19/7205optimal sensor placementgenetic algorithmunderwater vehicleFisher information matrix |
| spellingShingle | Murillo Villa Bruno Ferreira Nuno Cruz Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises Sensors optimal sensor placement genetic algorithm underwater vehicle Fisher information matrix |
| title | Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises |
| title_full | Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises |
| title_fullStr | Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises |
| title_full_unstemmed | Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises |
| title_short | Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises |
| title_sort | genetic algorithm to solve optimal sensor placement for underwater vehicle localization with range dependent noises |
| topic | optimal sensor placement genetic algorithm underwater vehicle Fisher information matrix |
| url | https://www.mdpi.com/1424-8220/22/19/7205 |
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