Discrimination of stationary from moving targets with recurrent neural networks in automotive Radar

Discrimination of stationary from moving targets with recurrent neural networks in automotive Radar

In this paper, we present a neural network based classification algorithm for the discrimination of moving from stationary targets in the sight of an automotive radar sensor. Compared to existing algorithms, the proposed algorithm can take into account multiple local radar targets instead of perform...

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Bibliographic Details
Published in:2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) pp. 1 - 4
Main Authors: Grimm, Christopher, Breddermann, Tobias, Farhoud, Ridha, Fei, Tai, Warsitz, Ernst, Haeb-Umbach, Reinhold
Format: Conference Proceeding
Language:English
Published: IEEE 01-04-2018
Subjects:
Automotive engineering
Automotive radar
classification
Inference algorithms
Legged locomotion
Neural networks
pedestrian
Radar detection
Recurrent Neural Network
Signal processing algorithms
stationary
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Summary:In this paper, we present a neural network based classification algorithm for the discrimination of moving from stationary targets in the sight of an automotive radar sensor. Compared to existing algorithms, the proposed algorithm can take into account multiple local radar targets instead of performing classification inference on each target individually resulting in superior discrimination accuracy, especially suitable for non rigid objects, like pedestrians, which in general have a wide velocity spread when multiple targets are detected.
DOI:10.1109/ICMIM.2018.8443525