Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization
This paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. In particular, the Proximity Policy Optimization (PPO) reinforcement algorithm is used to discover a policy for sensor selection that results i...
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| مؤلفون آخرون: | , , |
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2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1457 |
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| _version_ | 1857415064025300992 |
|---|---|
| author | De Rochechouart, Maxence |
| author2 | Segrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| author2_role | author author author |
| author_facet | De Rochechouart, Maxence Segrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| author_role | author |
| dc.creator.none.fl_str_mv | De Rochechouart, Maxence Segrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| dc.date.none.fl_str_mv | 2023 2024-01-03T06:13:55Z 2024-01-03T06:13:55Z |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1457 10.1109/RADAR54928.2023.10371080 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | 2023 IEEE International Radar Conference (RADAR) |
| dc.subject.none.fl_str_mv | Proximity Policy Optimization (PPO) Sensors Allocation Drone Tracking Fusion |
| dc.title.none.fl_str_mv | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedings |
| description | This paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. In particular, the Proximity Policy Optimization (PPO) reinforcement algorithm is used to discover a policy for sensor selection that results in optimum sensor resource allocation. In this work, one radar and one camera collaborate to generate tracks of a drone. The sequential measurements processing method is used to merge the inputs from the camera and the radar. An Extended Kalman Filter (EKF) is used to estimate the track. The learned system can apply sensor allocation online, works in real-time, and selects one radar and one camera at a time without having to reevaluate a cost function at every time step. Comparisons are done with the straight minimum entropy method and the random selection baseline method. The work demonstrates how machine learning techniques can capture resource allocation policy and help avoid the complexity of having to re-calculate cost function at every time step, especially when we have many radars and many cameras. |
| id | sorbonner_ee1b63e8f5ae5fbd5fe993ba068136de |
| identifier_str_mv | 10.1109/RADAR54928.2023.10371080 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1457 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy OptimizationDe Rochechouart, MaxenceSegrouchni, Amal El FallahBarbaresco, FredericAbu Zitar, RaedProximity Policy Optimization (PPO)Sensors AllocationDrone TrackingFusionThis paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. In particular, the Proximity Policy Optimization (PPO) reinforcement algorithm is used to discover a policy for sensor selection that results in optimum sensor resource allocation. In this work, one radar and one camera collaborate to generate tracks of a drone. The sequential measurements processing method is used to merge the inputs from the camera and the radar. An Extended Kalman Filter (EKF) is used to estimate the track. The learned system can apply sensor allocation online, works in real-time, and selects one radar and one camera at a time without having to reevaluate a cost function at every time step. Comparisons are done with the straight minimum entropy method and the random selection baseline method. The work demonstrates how machine learning techniques can capture resource allocation policy and help avoid the complexity of having to re-calculate cost function at every time step, especially when we have many radars and many cameras.2024-01-03T06:13:55Z2024-01-03T06:13:55Z2023Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedingshttps://depot.sorbonne.ae/handle/20.500.12458/145710.1109/RADAR54928.2023.10371080en2023 IEEE International Radar Conference (RADAR)oai:depot.sorbonne.ae:20.500.12458/14572024-01-03T06:13:55Z |
| spellingShingle | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization De Rochechouart, Maxence Proximity Policy Optimization (PPO) Sensors Allocation Drone Tracking Fusion |
| title | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| title_full | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| title_fullStr | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| title_full_unstemmed | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| title_short | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| title_sort | Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization |
| topic | Proximity Policy Optimization (PPO) Sensors Allocation Drone Tracking Fusion |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1457 |