Scene-adaptive radar tracking with deep reinforcement learning
Multi-target tracking with radars is a highly challenging problem due to detection artifacts, sensor noise, and interference sources. The traditional signal processing chain is, therefore, a complex combination of various algorithms with several tunable tracking-parameters. Usually, these are initia...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022000196 |
| _version_ | 1818551908733812736 |
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| author | Michael Stephan Lorenzo Servadei José Arjona-Medina Avik Santra Robert Wille Georg Fischer |
| author_facet | Michael Stephan Lorenzo Servadei José Arjona-Medina Avik Santra Robert Wille Georg Fischer |
| author_sort | Michael Stephan |
| collection | DOAJ |
| description | Multi-target tracking with radars is a highly challenging problem due to detection artifacts, sensor noise, and interference sources. The traditional signal processing chain is, therefore, a complex combination of various algorithms with several tunable tracking-parameters. Usually, these are initially set by engineers and are independent of the scene tracked. For this reason, they are often non-optimal and generate poorly performing tracking. In this context, scene-adaptive radar processing refers to algorithms that can sense, understand and learn information related to detected targets as well as the environment and adapt its tracking-parameters to optimize the desired goal. In this paper, we propose a Deep Reinforcement Learning framework that guides the scene-adaptive choice of radar tracking-parameters towards an improved performance on multi-target tracking. |
| first_indexed | 2024-12-12T09:06:09Z |
| format | Article |
| id | doaj.art-0d90c229f6114b4a92d7d9774cf6fc83 |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-12-12T09:06:09Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-0d90c229f6114b4a92d7d9774cf6fc832022-12-22T00:29:39ZengElsevierMachine Learning with Applications2666-82702022-06-018100284Scene-adaptive radar tracking with deep reinforcement learningMichael Stephan0Lorenzo Servadei1José Arjona-Medina2Avik Santra3Robert Wille4Georg Fischer5Infineon Technologies, Am Campeon 1-15, Neubiberg, 85579, Germany; Friedrich-Alexander-University Erlangen–Nuremberg, Schlossplatz 4, Erlangen, 91054, Germany; Corresponding author at: Friedrich-Alexander-University Erlangen–Nuremberg, Schlossplatz 4, Erlangen, 91054, Germany.Infineon Technologies, Am Campeon 1-15, Neubiberg, 85579, GermanyJohannes Kepler University Linz, Altenbergerstraße 69, Linz, 4040, Austria; RL Community, AI AUSTRIA, Wollzeile 24/12, Vienna, 1010, AustriaInfineon Technologies, Am Campeon 1-15, Neubiberg, 85579, GermanyJohannes Kepler University Linz, Altenbergerstraße 69, Linz, 4040, AustriaFriedrich-Alexander-University Erlangen–Nuremberg, Schlossplatz 4, Erlangen, 91054, GermanyMulti-target tracking with radars is a highly challenging problem due to detection artifacts, sensor noise, and interference sources. The traditional signal processing chain is, therefore, a complex combination of various algorithms with several tunable tracking-parameters. Usually, these are initially set by engineers and are independent of the scene tracked. For this reason, they are often non-optimal and generate poorly performing tracking. In this context, scene-adaptive radar processing refers to algorithms that can sense, understand and learn information related to detected targets as well as the environment and adapt its tracking-parameters to optimize the desired goal. In this paper, we propose a Deep Reinforcement Learning framework that guides the scene-adaptive choice of radar tracking-parameters towards an improved performance on multi-target tracking.http://www.sciencedirect.com/science/article/pii/S2666827022000196Reinforcement learningRadar trackingScene adaptation |
| spellingShingle | Michael Stephan Lorenzo Servadei José Arjona-Medina Avik Santra Robert Wille Georg Fischer Scene-adaptive radar tracking with deep reinforcement learning Machine Learning with Applications Reinforcement learning Radar tracking Scene adaptation |
| title | Scene-adaptive radar tracking with deep reinforcement learning |
| title_full | Scene-adaptive radar tracking with deep reinforcement learning |
| title_fullStr | Scene-adaptive radar tracking with deep reinforcement learning |
| title_full_unstemmed | Scene-adaptive radar tracking with deep reinforcement learning |
| title_short | Scene-adaptive radar tracking with deep reinforcement learning |
| title_sort | scene adaptive radar tracking with deep reinforcement learning |
| topic | Reinforcement learning Radar tracking Scene adaptation |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022000196 |
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