eprintid: 1508358
rev_number: 42
eprint_status: archive
userid: 608
dir: disk0/01/50/83/58
datestamp: 2017-01-12 15:26:27
lastmod: 2021-09-26 22:53:03
status_changed: 2017-05-04 13:12:08
type: article
metadata_visibility: show
creators_name: Ritchie, M
creators_name: Ash, M
creators_name: Chen, Q
creators_name: Chetty, K
title: Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
divisions: F52
keywords: Science & Technology, Physical Sciences, Technology, Chemistry, Analytical, Electrochemistry, Instruments & Instrumentation, Chemistry, micro-Doppler, FMCW radar, through-the-wall, classification, SIGNATURES, PERSONNEL, SVD, TRACKING, FEATURES, MOTIONS
note: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
abstract: The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques
date: 2016-09
date_type: published
publisher: MDPI AG
official_url: http://doi.org/10.3390/s16091401
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1154198
doi: 10.3390/s16091401
lyricists_name: Ash, Matthew
lyricists_name: Chen, Qingchao
lyricists_name: Chetty, Kevin
lyricists_name: Ritchie, Matthew
lyricists_id: MJASH24
lyricists_id: CHENK14
lyricists_id: KCHET45
lyricists_id: MARIT18
actors_name: Laslett, David
actors_id: DLASL34
actors_role: owner
full_text_status: public
publication: Sensors
volume: 16
number: 9
article_number: 1401
pages: 18
issn: 1424-8220
citation:        Ritchie, M;    Ash, M;    Chen, Q;    Chetty, K;      (2016)    Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis.                   Sensors , 16  (9)    , Article 1401.  10.3390/s16091401 <https://doi.org/10.3390/s16091401>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1508358/1/Chetty2_sensors-16-01401.pdf