Importance ranking of features for human micro-Doppler classification with a radar network

Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. A...

Full description

Saved in:
Bibliographic Details
Main Authors: Gurbuz, Sevgi Zubeyde, Tekeli, Burkan, Yuksel, Melda, Karabacak, Cesur, Gurbuz, Ali Cafer, Guldogan, Mehmet Burak
Format: Conference Proceeding
Language:English
Subjects:
Antennas
classification
Feature extraction
feature selection
human micro-Doppler
Legged locomotion
multistatic radar
Mutual information
Radar
radar network
Spectrogram
Torso
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. Although dozens of features have been proposed for these purposes, little work has examined the issue of which features are more important - i.e., have a greater impact on classification performance - than others. In this work, an information theoretic approach is applied to compute the importance ranking of features prior to classification for the specific problem of discriminating human walking from running. Results show that the ranking of features according to mutual information directly relates to classification performance using support vector machines.