Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks

Sangtae Kim, Kwangjin Lee, Seungho Doo, Byonghyo Shim

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

    4 Citations (Scopus)

    Abstract

    In this paper, we propose a deep neural network model for target classification in automotive radar system. In the proposed network, we introduce transposed convolutional network (TCNet) which applies transposed convolution operations. We discuss the properties of transposed convolution and show that TCNet can reduce the network size and improve the classification performance for the systems in which the signals are sparse and memory is restricted like our automotive radar systems. In our experiment, we show that the proposed network outperforms other popularly used dimensionality reduction approaches in terms of classification accuracy.

    Original languageEnglish
    Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
    EditorsMichael B. Matthews
    PublisherIEEE Computer Society
    Pages2050-2054
    Number of pages5
    ISBN (Electronic)9781538692189
    DOIs
    Publication statusPublished - 2018 Jul 2
    Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
    Duration: 2018 Oct 282018 Oct 31

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    Volume2018-October
    ISSN (Print)1058-6393

    Conference

    Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
    Country/TerritoryUnited States
    CityPacific Grove
    Period18/10/2818/10/31

    Bibliographical note

    Publisher Copyright:
    © 2018 IEEE.

    Keywords

    • classification
    • convolutional neural networks
    • fast chirp FMCW radar
    • recurrent neural networks

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Networks and Communications

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