Multiple underwater target positioning with optimally placed acoustic surface sensor networks
In an increasing number of scientific and commercial mission scenarios at sea, it is required to simultaneously localize a group of underwater targets. The latter may include moored systems, autonomous vehicles, and even human divers. For reasons that have to do with the unavailability of Global Pos...
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| Format: | Article |
| Language: | English |
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Wiley
2018-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718773234 |
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| author | David Moreno-Salinas Antonio Manuel Pascoal Joaquín Aranda |
| author_facet | David Moreno-Salinas Antonio Manuel Pascoal Joaquín Aranda |
| author_sort | David Moreno-Salinas |
| collection | DOAJ |
| description | In an increasing number of scientific and commercial mission scenarios at sea, it is required to simultaneously localize a group of underwater targets. The latter may include moored systems, autonomous vehicles, and even human divers. For reasons that have to do with the unavailability of Global Positioning System underwater, cost reduction, and simplicity of operation, there is currently a surge of interest in the development of range-based multiple target localization systems that rely on the computation of the distances between the targets and a number of sensor nodes deployed at the ocean surface, equipped with acoustic range measuring devices. In the case of a single target, there is a wealth of literature on the problem of optimal acoustic sensor placement to maximize the information available for target localization using trilateration methods. In the case of multiple targets, however, the literature is scarce. Motivated by these considerations, we address the problem of optimal sensor placement for multiple underwater target positioning. In this setup, we are naturally led to a multiple objective optimization problem, the solution of which allows for the analysis of the trade-offs involved in the localization of the targets simultaneously. To this end, we resort to tools from estimation theory and multi-objective optimization. For each target, the function to be minimized (by proper choice of the sensor configuration) is related to the determinant of the corresponding Fisher information matrix, which yields information on the minimum possible covariance of the error with which the position of the target can be estimated using any non-biased estimator. To deal with the fact that more than one target is involved, we exploit the concept of multiple objective Pareto-optimal solutions to characterize the best possible accuracy with which each of the targets can be positioned, given constraints on the desired positioning accuracy of the other targets. Simulation examples illustrate how, for a three-sensor network and two targets, it is possible to define an optimal sensor configuration that yields large positioning accuracy for both targets simultaneously, using convex optimization tools. When more than two targets are involved, however, more than three sensors are required to exploit an adequate trade-off of the accuracy with which each target can be positioned by resorting to non-convex and Pareto-optimization tools. We show how in this case the optimal sensor configurations depend on the Pareto weights assigned to each of the targets, as well as on the number of sensors, the number of targets, and the uncertainty with which the positions of the targets are known a priori. |
| format | Article |
| id | doaj-art-0ba076f960174698b5b1681e86c79b33 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-0ba076f960174698b5b1681e86c79b332025-02-03T05:55:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-05-011410.1177/1550147718773234Multiple underwater target positioning with optimally placed acoustic surface sensor networksDavid Moreno-Salinas0Antonio Manuel Pascoal1Joaquín Aranda2Department of Computer Science and Automatic Control, National University Distance Education (UNED), Madrid, SpainLaboratory for Robotics and Engineering Systems (LARSyS), Institute for Systems and Robotics (ISR), Instituto Superior Tecnico (IST), University of Lisbon, Lisbon, PortugalDepartment of Computer Science and Automatic Control, National University Distance Education (UNED), Madrid, SpainIn an increasing number of scientific and commercial mission scenarios at sea, it is required to simultaneously localize a group of underwater targets. The latter may include moored systems, autonomous vehicles, and even human divers. For reasons that have to do with the unavailability of Global Positioning System underwater, cost reduction, and simplicity of operation, there is currently a surge of interest in the development of range-based multiple target localization systems that rely on the computation of the distances between the targets and a number of sensor nodes deployed at the ocean surface, equipped with acoustic range measuring devices. In the case of a single target, there is a wealth of literature on the problem of optimal acoustic sensor placement to maximize the information available for target localization using trilateration methods. In the case of multiple targets, however, the literature is scarce. Motivated by these considerations, we address the problem of optimal sensor placement for multiple underwater target positioning. In this setup, we are naturally led to a multiple objective optimization problem, the solution of which allows for the analysis of the trade-offs involved in the localization of the targets simultaneously. To this end, we resort to tools from estimation theory and multi-objective optimization. For each target, the function to be minimized (by proper choice of the sensor configuration) is related to the determinant of the corresponding Fisher information matrix, which yields information on the minimum possible covariance of the error with which the position of the target can be estimated using any non-biased estimator. To deal with the fact that more than one target is involved, we exploit the concept of multiple objective Pareto-optimal solutions to characterize the best possible accuracy with which each of the targets can be positioned, given constraints on the desired positioning accuracy of the other targets. Simulation examples illustrate how, for a three-sensor network and two targets, it is possible to define an optimal sensor configuration that yields large positioning accuracy for both targets simultaneously, using convex optimization tools. When more than two targets are involved, however, more than three sensors are required to exploit an adequate trade-off of the accuracy with which each target can be positioned by resorting to non-convex and Pareto-optimization tools. We show how in this case the optimal sensor configurations depend on the Pareto weights assigned to each of the targets, as well as on the number of sensors, the number of targets, and the uncertainty with which the positions of the targets are known a priori.https://doi.org/10.1177/1550147718773234 |
| spellingShingle | David Moreno-Salinas Antonio Manuel Pascoal Joaquín Aranda Multiple underwater target positioning with optimally placed acoustic surface sensor networks International Journal of Distributed Sensor Networks |
| title | Multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| title_full | Multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| title_fullStr | Multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| title_full_unstemmed | Multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| title_short | Multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| title_sort | multiple underwater target positioning with optimally placed acoustic surface sensor networks |
| url | https://doi.org/10.1177/1550147718773234 |
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