Convolutional Long Short-Term Memory Networks for Doppler-Radar Based Target Classification

被引：10

作者：

Khalid, Habib-ur-Rehman ^{[1
,2
]}

Pollin, Sofie ^{[1
,3
]}

Rykunov, Maxim ^{[1
]}

Bourdoux, Andre ^{[1
]}

Sahli, Hichem ^{[1
,2
]}

机构：

[1] Interuniv Microelect Ctr IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium

[2] Vrije Univ Brussel VUB, Pl Laan 2, B-1050 Brussels, Belgium

[3] Katholieke Univ Leuven KU Leuven, Kasteelpk Arenberg 10, B-3001 Leuven, Belgium

来源：

2019 IEEE RADAR CONFERENCE (RADARCONF) | 2019年

关键词：

D O I：

10.1109/radar.2019.8835731

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper investigates the use of range and Doppler features for target classification in Advanced Driver-Assistance Systems (ADAS). Pedestrians and bicyclists dataset was recorded using a 77 GHz Frequency Modulated Continuous Wave (FMCW) RAdio Detection And Ranging (RADAR) sensor. A Convolutional Long Short-Term Memory (CLSTM) based deep-learning model was proposed and evaluated with the recorded dataset. The proposed model uses a novel convolutional layer based feature compression method. Our proposed model was shown to generalize well to the dataset and was shown to perform with an average accuracy of 94.76% over the test subset.

引用

页数：6

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