Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks

Sangtae Kim; Kwangjin Lee; Seungho Doo; Byonghyo Shim

doi:10.1109/ACSSC.2018.8645406

Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks

Sangtae Kim, Kwangjin Lee, Seungho Doo, Byonghyo Shim

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English
Title of host publication	Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	2050-2054
Number of pages	5
ISBN (Electronic)	9781538692189
DOIs	https://doi.org/10.1109/ACSSC.2018.8645406
Publication status	Published - 2019 Feb 19
Event	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: 2018 Oct 28 → 2018 Oct 31

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2018-October
ISSN (Print)	1058-6393

Conference

Conference	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/Territory	United States
City	Pacific Grove
Period	18/10/28 → 18/10/31

Keywords

classification
convolutional neural networks
fast chirp FMCW radar
recurrent neural networks

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2018.8645406

Cite this

Kim, S., Lee, K., Doo, S., & Shim, B. (2019). Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 2050-2054). [8645406] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645406

Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks. / Kim, Sangtae; Lee, Kwangjin; Doo, Seungho et al.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2019. p. 2050-2054 8645406 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, S, Lee, K, Doo, S & Shim, B 2019, Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645406, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 2050-2054, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 18/10/28. https://doi.org/10.1109/ACSSC.2018.8645406

Kim S, Lee K, Doo S, Shim B. Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2019. p. 2050-2054. 8645406. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2018.8645406

Kim, Sangtae ; Lee, Kwangjin ; Doo, Seungho et al. / Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks. Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. editor / Michael B. Matthews. IEEE Computer Society, 2019. pp. 2050-2054 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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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.",

keywords = "classification, convolutional neural networks, fast chirp FMCW radar, recurrent neural networks",

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Moving Target Classification in Automotive Radar Systems Using Transposed Convolutional Networks

Abstract

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Keywords

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