Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even th...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 54; H. 4; S. 1709 - 1723
Hauptverfasser:	Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Activity recognition Artificial neural networks Assisted living Classification Continuous wave radar Convolutional autoencoder (CAE) deep learning Diagnostic systems Feature recognition gait recognition Legged locomotion Machine learning micro-Doppler Neural networks Pedestrian safety Radar Radar signatures Remote monitoring Spectrogram Support vector machines
ISSN:	0018-9251, 1557-9603
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!