Support vector machines for classification of automotive radar interference

Renyuan Zhang, Siyang Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper studies classification of the automotive radar interference waveforms via support vector machine (SVM). Automotive radar implemented in advanced driver assistance systems (ADASs) is an essential sensor in road traffic safety, e.g., moving target indication, collision avoidance and enhanced navigation system. However, radar-to-radar interference is inevitable as the number of automotive radar increases. Our work shows different types of radar-to-radar interference with analyzing the received signal. Providing linear frequency modulated transmitting (LFM) signal, filtering and dechirping techniques, the classification of six different types of radar-to-radar interference are presented and analyzed. Time-frequency domain signal and range-doppler profiles of different types of interference are simulated. The machine learning classifier of SVM of multi-class high-dimensional waveform data classification is used to classify different interference waveforms and non-interference waveforms. Random produced dataset is cross validated through the SVM classifier. Different types of interference prediction accuracies are shown and verified by the proposed method. The confusion matrix of interference and noninterference, detecting different types of interference and classifying all incoming receiving signal are presented.

Original languageEnglish (US)
Title of host publication2018 IEEE Radar Conference, RadarConf 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-371
Number of pages6
ISBN (Electronic)9781538641675
DOIs
StatePublished - Jun 8 2018
Event2018 IEEE Radar Conference, RadarConf 2018 - Oklahoma City, United States
Duration: Apr 23 2018Apr 27 2018

Publication series

Name2018 IEEE Radar Conference, RadarConf 2018

Conference

Conference2018 IEEE Radar Conference, RadarConf 2018
Country/TerritoryUnited States
CityOklahoma City
Period4/23/184/27/18

Keywords

  • DSP
  • SVM
  • automotive radar
  • classification
  • interference
  • machine learning
  • waveform processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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