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

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...

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Bibliographic Details
Main Authors: Khalid, Habib-ur-Rehman, Pollin, Sofie, Rykunov, Maxim, Bourdoux, Andre, Sahli, Hichem
Format: Conference Proceeding
Language:English
Subjects:
Complexity theory
Doppler effect
Doppler radar
Feature extraction
Hidden Markov models
Radar detection
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Summary: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.
ISSN:2375-5318
DOI:10.1109/RADAR.2019.8835731