Bespoke Simulator for Human Activity Classification with Bistatic Radar

Kai Yang, Qammer H. Abbasi, Francesco Fioranelli, Olivier Romain, Julien Le Kernec*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Radar is now widely used in human activity classification because of its contactless sensing capabilities, robustness to light conditions and privacy preservation compared to plain optical images. It has great value in elderly care, monitoring accidental falls and abnormal behaviours. Monostatic radar suffers from degradation in performance with varying aspect angles with respect to the target. Bistatic radar may offer a solution to this problem but finding the right geometry can be quite resource-intensive. We propose a bespoke simulation framework to test the radar geometry for human activity recognition. First, the analysis focuses on the monostatic radar model based on the Doppler effect in radar. We analyse the spectrogram of different motions by Short-time Fourier analysis (STFT), and then the classification data set was built for feature extraction and classification. The results show that the monostatic radar system has the highest accuracy, up to 98.17%. So, a bistatic radar model with separate transmitter and receiver was established in the experiment, and results show that bistatic radar with specific geometry configuration (CB2.5) not only has higher classification accuracy than monostatic radar in each aspect angle but also can recognise the object in a wider angle range. After training and fusing the data of all angles, it is found that the accuracy, sensitivity, and specificities of CB2.5 have 2.2%, 7.7% and 1.5% improvement compared with monostatic radar.

Original languageEnglish
Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
EditorsMasood Ur Rehman, Ahmed Zoha
Number of pages15
ISBN (Print)9783030955922
Publication statusPublished - 2022
Event16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online
Duration: 25 Dec 202126 Dec 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume420 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference16th EAI International Conference on Body Area Networks, BODYNETS 2021
CityVirtual, Online

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Human activity recognition
  • Micro-Doppler
  • Radar
  • Radar signature simulation


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