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Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for...

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Detalles Bibliográficos
Autores principales: Tirri, Anna Elena, Fasano, Giancarmine, Accardo, Domenico, Moccia, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102095/
https://www.ncbi.nlm.nih.gov/pubmed/25105154
http://dx.doi.org/10.1155/2014/280478
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author Tirri, Anna Elena
Fasano, Giancarmine
Accardo, Domenico
Moccia, Antonio
author_facet Tirri, Anna Elena
Fasano, Giancarmine
Accardo, Domenico
Moccia, Antonio
author_sort Tirri, Anna Elena
collection PubMed
description Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection.
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spelling pubmed-41020952014-08-07 Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications Tirri, Anna Elena Fasano, Giancarmine Accardo, Domenico Moccia, Antonio ScientificWorldJournal Research Article Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection. Hindawi Publishing Corporation 2014 2014-07-01 /pmc/articles/PMC4102095/ /pubmed/25105154 http://dx.doi.org/10.1155/2014/280478 Text en Copyright © 2014 Anna Elena Tirri et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tirri, Anna Elena
Fasano, Giancarmine
Accardo, Domenico
Moccia, Antonio
Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title_full Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title_fullStr Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title_full_unstemmed Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title_short Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
title_sort particle filtering for obstacle tracking in uas sense and avoid applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102095/
https://www.ncbi.nlm.nih.gov/pubmed/25105154
http://dx.doi.org/10.1155/2014/280478
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