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Article Dans Une Revue IEEE Sensors Journal Année : 2021

Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection

Résumé

The development of safe intelligent transportation systems (ITS) has driven extensive research to come up with efficient environment perception techniques with a variety of sensors. In short range settings, Ultra Wide-Band (UWB) radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant regions of interest (ROIs) within its signals. This article proposes a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar. Specifically, we implement a differential entropy analysis to detect ROIs. We evaluate our technique on a benchmark of more than 47 thousands samples. The obtained results show higher performance in terms of obstacle detection compared to state-of-the-art techniques, and a stable robustness even with low amplitude signals.
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Dates et versions

hal-03528707 , version 1 (17-01-2022)

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Citer

Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Najoua Essoukri Ben Amara, Atika Rivenq, et al.. Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection. IEEE Sensors Journal, 2021, 21 (6), pp.8142-8149. ⟨10.1109/JSEN.2021.3050054⟩. ⟨hal-03528707⟩
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