Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications
Joint Authors
Tirri, Anna Elena
Moccia, Antonio
Fasano, Giancarmine
Accardo, Domenico
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Tirri, Anna Elena& Fasano, Giancarmine& Accardo, Domenico& Moccia, Antonio. 2014. Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049054
Modern Language Association (MLA)
Tirri, Anna Elena…[et al.]. Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1049054
American Medical Association (AMA)
Tirri, Anna Elena& Fasano, Giancarmine& Accardo, Domenico& Moccia, Antonio. Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049054
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049054