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Region based Single-Stage Interference Mitigation and Target Detection
Publikationstyp
Conference Paper
Date Issued
2020-09-21
Sprache
English
TORE-URI
Article Number
9266434
Citation
IEEE National Radar Conference (2020)
Contribution to Conference
Publisher DOI
Scopus ID
The inherent smaller radar cross sections of vulnerable road users resulting in smaller signal-to-noise-ratios make an accurate detection of them somewhat challenging. Mutual radar interference in typical automotive scenarios further imposes the difficulty of a target detection by additionally raising the noise floor. The traditional signal processing pipeline consists of multiple but separate stages for interference detection, mitigation and target detection. In this paper, a convolutional neural network based autoencoder architecture is used to perform a combined single-stage target detection while generalizing over different interference noise. The proposed approach achieves significant improvement over state-of-the-art methods while preserving the instance of each target and is able to identify them uniquely in case of a partial occlusion or overlapping of multiple targets.
Subjects
automotive radar
convolutional - autoencoder
interference mitigation
VRU detection