Automatic Multipath Annotation for Conventional Automotive Radar Datasets

Stav Danino, Igal Bilik

Research output: Contribution to journalArticlepeer-review

Abstract

Automotive radars operating in dense urban environments experience a multipath propagation phenomenon, degrading radar performance, and challenging conventional radar processing. The deep learning (DL)-based automotive radar processing that can address multipath-induced challenges requires the availability of large and accurately annotated datasets. Publically available automotive radar datasets are missing accurate labeling of the multipath-induced 'ghost' targets and, therefore, cannot be used for DL-based automotive radar processing. This work proposes the automated multipath annotation approach to convert conventional large databases into multipath-labeled datasets. The proposed approach provides the fine-granularity 'ghost' targets and reflector labels to distinguish between the actual target and the reflector, to identify the reflector type, and to estimate the multipath reflection order. The multipath annotation performance of the proposed approach is evaluated using datasets of radar measurements recorded in typical automotive scenarios and a synthetic dataset of complex multitarget scenarios. It is shown that the proposed annotation approach can effectively annotate multipath radar detections in practical automotive scenarios and thus enables DL-based automotive radar processing in multipath-dominated urban environments.

Original languageEnglish
Pages (from-to)13500-13517
Number of pages18
JournalIEEE Sensors Journal
Volume24
Issue number8
DOIs
StatePublished - 15 Apr 2024

Keywords

  • Automatic annotation
  • automotive radar
  • automotive radar dataset
  • deep neural network (DNN)-based radar processing
  • multipath labeling

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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