Multistatic Human Micro-Doppler Classification with Degraded/Jammed Radar Data

Fioranelli, F. , Patel, J. , Gürbüz, S. Z., Ritchie, M. and Griffiths, H. (2019) Multistatic Human Micro-Doppler Classification with Degraded/Jammed Radar Data. In: IEEE Radar Conference, Boston, MA, USA, 22-26 Apr 2019, (Accepted for Publication)

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This paper investigates the classification performance of using multistatic human micro-Doppler radar data that have been degraded by some form of jamming. Two simple cases of Signal-to-Noise Ratio (SNR) degradation and nulling of a sub-set of the available radar pulses are considered for these initial results, leaving more complex forms of jamming for future work. Experimental data collected with a multistatic radar are used in this study, aiming to classify 7 similar human activities, when individual subjects are walking carrying different objects. The results show that the use of multistatic radar data can provide resilience to the effect of the data degradation, thanks to the redundancy and additional information available from multiple radar nodes.

Item Type:Conference Proceedings
Status:Accepted for Publication
Glasgow Author(s) Enlighten ID:Patel, Mr Jarez and Ritchie, Mr Matthew and Fioranelli, Dr Francesco
Authors: Fioranelli, F., Patel, J., Gürbüz, S. Z., Ritchie, M., and Griffiths, H.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy