Preprint Article Version 1 NOT YET PEER-REVIEWED

Through Wall Radar Classification of Human Micro-Doppler using Singular Value Decomposition Analysis

  1. Department of Electronic and Electrical Engineering, University College London, London WC1E 7JE, UK
  2. Department of Security and Crime Science, University College London, London WC1H 9EZ, UK
Version 1 : Received: 30 August 2016 / Approved: 31 August 2016 / Online: 31 August 2016 (09:02:32 CEST)

A peer-reviewed article of this Preprint also exists.

Ritchie, M.; Ash, M.; Chen, Q.; Chetty, K. Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis. Sensors 2016, 16, 1401. Ritchie, M.; Ash, M.; Chen, Q.; Chetty, K. Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis. Sensors 2016, 16, 1401.

Journal reference: Sensors 2016, 16, 1401
DOI: 10.3390/s16091401

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.

Subject Areas

micro-Doppler; FMCW radar; through-the-wall; classification

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