Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band
Abstract The Doppler signature of a man walking in a forested area analysed at L‐band is presented here. The aim is twofold: to assess the best time‐frequency distribution to characterise the activity; to highlight the similarity of the simulated data to the measured ones to validate the simulation...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12147 |
| _version_ | 1811261323108941824 |
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| author | Giovanni Manfredi Israel D. Hinostroza Sáenz Michel Menelle Stéphane Saillant Jean‐Philippe Ovarlez Laetitia Thirion‐Lefevre |
| author_facet | Giovanni Manfredi Israel D. Hinostroza Sáenz Michel Menelle Stéphane Saillant Jean‐Philippe Ovarlez Laetitia Thirion‐Lefevre |
| author_sort | Giovanni Manfredi |
| collection | DOAJ |
| description | Abstract The Doppler signature of a man walking in a forested area analysed at L‐band is presented here. The aim is twofold: to assess the best time‐frequency distribution to characterise the activity; to highlight the similarity of the simulated data to the measured ones to validate the simulation tool. Indeed, the Doppler‐Time (DT) signal variation represents the main characteristic of Artificial Neural Networks (ANNs) for classification. The more accurately the DT characterises the activity, the higher the machine’s accuracy in classifying it. Besides, in the training data frame, reliable simulated models may supply the amount of data needed by ANN applications. Thus, a short‐time Fourier transform (STFT), a reassigned spectrogram (RE‐Spect), and a pseudo‐Wigner–Ville distribution have been applied to the measured and simulated data. The measurements have been performed using a bistatic radar working at 1 GHz. Then, the measurement setup has been replicated in simulation, and 3‐D human bodies walking in free space have been computed using physical optics. The results show that the STFT is the most suitable time‐frequency method for recognising and classifying the walk. Moreover, the simulated data are in agreement with the measured data, regardless of the chosen Cohen’s technique. |
| first_indexed | 2024-04-12T19:02:31Z |
| format | Article |
| id | doaj.art-c1a64117c8fb4d43886829d17c16b7a3 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-04-12T19:02:31Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-c1a64117c8fb4d43886829d17c16b7a32022-12-22T03:20:07ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922021-12-0115121573158210.1049/rsn2.12147Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐bandGiovanni Manfredi0Israel D. Hinostroza Sáenz1Michel Menelle2Stéphane Saillant3Jean‐Philippe Ovarlez4Laetitia Thirion‐Lefevre5SONDRA CentraleSupélec Université Paris‐Saclay Gif‐sur‐Yvette FranceSONDRA CentraleSupélec Université Paris‐Saclay Gif‐sur‐Yvette FranceONERA Université Paris‐Saclay (DEMR/EGDR) Palaiseau FranceSONDRA CentraleSupélec Université Paris‐Saclay Gif‐sur‐Yvette FranceSONDRA CentraleSupélec Université Paris‐Saclay Gif‐sur‐Yvette FranceSONDRA CentraleSupélec Université Paris‐Saclay Gif‐sur‐Yvette FranceAbstract The Doppler signature of a man walking in a forested area analysed at L‐band is presented here. The aim is twofold: to assess the best time‐frequency distribution to characterise the activity; to highlight the similarity of the simulated data to the measured ones to validate the simulation tool. Indeed, the Doppler‐Time (DT) signal variation represents the main characteristic of Artificial Neural Networks (ANNs) for classification. The more accurately the DT characterises the activity, the higher the machine’s accuracy in classifying it. Besides, in the training data frame, reliable simulated models may supply the amount of data needed by ANN applications. Thus, a short‐time Fourier transform (STFT), a reassigned spectrogram (RE‐Spect), and a pseudo‐Wigner–Ville distribution have been applied to the measured and simulated data. The measurements have been performed using a bistatic radar working at 1 GHz. Then, the measurement setup has been replicated in simulation, and 3‐D human bodies walking in free space have been computed using physical optics. The results show that the STFT is the most suitable time‐frequency method for recognising and classifying the walk. Moreover, the simulated data are in agreement with the measured data, regardless of the chosen Cohen’s technique.https://doi.org/10.1049/rsn2.12147time‐frequency analysisneural netsDoppler radarFourier transformsWigner distributionradar signal processing |
| spellingShingle | Giovanni Manfredi Israel D. Hinostroza Sáenz Michel Menelle Stéphane Saillant Jean‐Philippe Ovarlez Laetitia Thirion‐Lefevre Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band IET Radar, Sonar & Navigation time‐frequency analysis neural nets Doppler radar Fourier transforms Wigner distribution radar signal processing |
| title | Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band |
| title_full | Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band |
| title_fullStr | Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band |
| title_full_unstemmed | Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band |
| title_short | Time‐frequency characterisation of bistatic Doppler signature of a wooded area walk at L‐band |
| title_sort | time frequency characterisation of bistatic doppler signature of a wooded area walk at l band |
| topic | time‐frequency analysis neural nets Doppler radar Fourier transforms Wigner distribution radar signal processing |
| url | https://doi.org/10.1049/rsn2.12147 |
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