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 | |
Publication status | Published - 2018 Jul 2 |
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 |
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