A Cognitive FMCW Radar to Minimize a Sequence of Range-Doppler Measurements
| Autor: | Peter Ott, Marco Altmann, Christian Waldschmidt, Nicolaj C. Stache, Dmitrii Kozlov |
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| Rok vydání: | 2021 |
| Předmět: |
Sequence
Computer science business.industry Deep learning Real-time computing Bandwidth (signal processing) 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences 01 natural sciences law.invention Continuous-wave radar law 0202 electrical engineering electronic engineering information engineering Chirp Reinforcement learning ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Artificial intelligence Radar business Energy (signal processing) 0105 earth and related environmental sciences |
| Zdroj: | 2020 17th European Radar Conference (EuRAD). |
| DOI: | 10.1109/eurad48048.2021.00065 |
| Popis: | This paper proposes a cognitive radar setup to learn the minimal sequence of Range-Doppler measurements for accurate multi-target detection with adaptive parameters. This minimal measurement sequence is achieved by a novel reward definition in a Reinforcement Learning approach. Thus, the cognitive radar learns to optimize its measurement time and energy savings. Based on Range-Doppler maps, the Reinforcement Learning agent adapts the FMCW parameters like bandwidth, sweep time, chirp repetition time and number of chirps to optimize the recognition in a three-target scenario. The agent is trained using Proximal Policy Optimization (PPO) in a simulated radar environment. |
| Databáze: | OpenAIRE |
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