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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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/280478 |
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| author | Anna Elena Tirri Giancarmine Fasano Domenico Accardo Antonio Moccia |
| author_facet | Anna Elena Tirri Giancarmine Fasano Domenico Accardo Antonio Moccia |
| author_sort | Anna Elena Tirri |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-4e5930d28adf46a19f75d959c8399085 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4e5930d28adf46a19f75d959c83990852025-02-03T01:24:12ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/280478280478Particle Filtering for Obstacle Tracking in UAS Sense and Avoid ApplicationsAnna Elena Tirri0Giancarmine Fasano1Domenico Accardo2Antonio Moccia3University of Naples “Federico II”, I80125 Naples, ItalyUniversity of Naples “Federico II”, I80125 Naples, ItalyUniversity of Naples “Federico II”, I80125 Naples, ItalyUniversity of Naples “Federico II”, I80125 Naples, ItalyObstacle 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.http://dx.doi.org/10.1155/2014/280478 |
| spellingShingle | Anna Elena Tirri Giancarmine Fasano Domenico Accardo Antonio Moccia Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2014/280478 |
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