Classification of human motions using empirical mode decomposition of human micro‐Doppler signatures

IEEE Trans. Aerosp. Electron. Syst.

Gürbüz

et al. 2017

Feature diversity for optimized human micro-doppler classification using multistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 53(2), pp. 640-654. (doi:10.1109/TAES.2017.2651678) This is the author's final accepted version.There may be differences between this version and the published version. FEATURE DIVERSITY FOR OPTIMIZED HUMAN MICRO-DOPPLER CLASSIFICATION USING MULTISTATIC RADARFrancesco Fioranelli (1) , Matthew Ritchie (2) , Sevgi Zübeyde Gürbüz (3) , Hugh Griffiths (2) ( AbstractThis paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes.

Section: Introductionmentioning

confidence: 99%

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fioranelli

IEEE Trans. Aerosp. Electron. Syst.

Gürbüz

et al. 2017

“…Micro-Doppler signatures have been also analysed with alternative approaches, such as the Hilbert-Huang transform [10], empirical mode decomposition [11], the Pseudo Wigner-Ville Distribution and B-Distribution [12].…”

Section: Introductionmentioning

confidence: 99%

Aspect angle dependence and multistatic data fusion for micro‐Doppler classification of armed/unarmed personnel

Fioranelli

IET Radar, Sonar & Navigation

Griffiths

2015

This paper discusses the analysis of multistatic micro-Doppler signatures and related features to distinguish and classify unarmed and potentially armed personnel. The application of radar systems to distinguish different motion types has been previously proposed and this work aims to further investigate the applicability of this in more scenarios. Real data have been collected using a multistatic radar system in a series of experiments involving several individuals performing different movements. Changes in classification accuracy as a function of different aspect angle between the direction in which the target faces and the line-of-sight of the radar nodes are analysed. Multiple data fusion methodologies are proposed, showing that significant improvement of the classification accuracy can be achieved when using separate classification at each node followed by a voting procedure to reach the final decision. This is beneficial especially at those aspect angles for which micro-Doppler detection is less favourable.

“…The focus of these techniques is often on the maximization the signal-to-noise ratio (SNR) of the desired target or removal of the significant primary wall reflection. TTW human micro-Doppler signatures have been both simulated and analysed previously within [16]- [19]. In comparison with those publications, this article focuses on the application of classification of movements on real data obtained in a through wall configuration using human micro-Doppler signatures.…”

Section: Introductionmentioning

confidence: 99%

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

Ash

Chen

et al. 2016

Preprint

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.