Autonomous Tracking of Intermittent RF Source Using a UAV Swarm
The localization of a radio-frequency transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength sensors. This group embarks on an autonomous patrol to localize and track the target with a specified...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8304570/ |
| _version_ | 1818912964463296512 |
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| author | Farshad Koohifar Ismail Guvenc Mihail L. Sichitiu |
| author_facet | Farshad Koohifar Ismail Guvenc Mihail L. Sichitiu |
| author_sort | Farshad Koohifar |
| collection | DOAJ |
| description | The localization of a radio-frequency transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization) and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage, we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection-based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare a steepest descent posterior Cramer–Rao lower bound path planning and a bioinspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bioinspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection-based EKF. |
| first_indexed | 2024-12-19T23:22:57Z |
| format | Article |
| id | doaj.art-16ebb9fccfa5489d89141644ff902aa8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:22:57Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-16ebb9fccfa5489d89141644ff902aa82022-12-21T20:01:55ZengIEEEIEEE Access2169-35362018-01-016158841589710.1109/ACCESS.2018.28105998304570Autonomous Tracking of Intermittent RF Source Using a UAV SwarmFarshad Koohifar0https://orcid.org/0000-0002-6710-1370Ismail Guvenc1Mihail L. Sichitiu2Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USAThe localization of a radio-frequency transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization) and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage, we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection-based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare a steepest descent posterior Cramer–Rao lower bound path planning and a bioinspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bioinspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection-based EKF.https://ieeexplore.ieee.org/document/8304570/Cramer Rao lower bounddroneFisher informationintermittent transmitterjammerlocalization |
| spellingShingle | Farshad Koohifar Ismail Guvenc Mihail L. Sichitiu Autonomous Tracking of Intermittent RF Source Using a UAV Swarm IEEE Access Cramer Rao lower bound drone Fisher information intermittent transmitter jammer localization |
| title | Autonomous Tracking of Intermittent RF Source Using a UAV Swarm |
| title_full | Autonomous Tracking of Intermittent RF Source Using a UAV Swarm |
| title_fullStr | Autonomous Tracking of Intermittent RF Source Using a UAV Swarm |
| title_full_unstemmed | Autonomous Tracking of Intermittent RF Source Using a UAV Swarm |
| title_short | Autonomous Tracking of Intermittent RF Source Using a UAV Swarm |
| title_sort | autonomous tracking of intermittent rf source using a uav swarm |
| topic | Cramer Rao lower bound drone Fisher information intermittent transmitter jammer localization |
| url | https://ieeexplore.ieee.org/document/8304570/ |
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