DOAJ Open Access 2022

Scene-adaptive radar tracking with deep reinforcement learning

Michael Stephan Lorenzo Servadei José Arjona-Medina Avik Santra Robert Wille +1 lainnya

Abstrak

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.

Topik & Kata Kunci

Cybernetics Electronic computers. Computer science

Penulis (6)

Michael Stephan

Lorenzo Servadei

José Arjona-Medina

Avik Santra

Robert Wille

Georg Fischer

Format Sitasi

APA MLA BibTeX

Stephan, M., Servadei, L., Arjona-Medina, J., Santra, A., Wille, R., Fischer, G. (2022). Scene-adaptive radar tracking with deep reinforcement learning. https://doi.org/10.1016/j.mlwa.2022.100284

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Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.1016/j.mlwa.2022.100284
Akses: Open Access ✓