Autoregressive Modelling Techniques for Enhanced TWRI

Salih Vehbi Comert; Isin Erer

doi:10.1109/CAMA49227.2021.9703483

Autoregressive Modelling Techniques for Enhanced TWRI

Salih Vehbi Comert, Isin Erer

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

High resolution methods such as spectral estimation or compressive sensing based ones have been proposed for through-wall radar (TWR) since conventional beam-forming approach suffers from high side-lobes besides low resolution. In this study auto-regressive (AR) modeling is applied to TWR data after beamforming to enhance image quality. The results are compared with existing beam-space MUSIC and compressive sensing based methods in terms of target detectability.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	398-401
Sayfa sayısı	4
ISBN (Elektronik)	9781728196978
DOI'lar	https://doi.org/10.1109/CAMA49227.2021.9703483
Yayın durumu	Yayınlandı - 2021
Etkinlik	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021 - Antibes Juan-les-Pins, France Süre: 15 Kas 2021 → 17 Kas 2021

Yayın serisi

Adı	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
Ülke/Bölge	France
Şehir	Antibes Juan-les-Pins
Periyot	15/11/21 → 17/11/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Belgeye Erişim

10.1109/CAMA49227.2021.9703483

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{5c5f47ff309d48648af342249f4acc91,

title = "Autoregressive Modelling Techniques for Enhanced TWRI",

abstract = "High resolution methods such as spectral estimation or compressive sensing based ones have been proposed for through-wall radar (TWR) since conventional beam-forming approach suffers from high side-lobes besides low resolution. In this study auto-regressive (AR) modeling is applied to TWR data after beamforming to enhance image quality. The results are compared with existing beam-space MUSIC and compressive sensing based methods in terms of target detectability.",

keywords = "autoregressive modeling, MUSIC spectral estimate, spectral estimation, Through the wall radar",

author = "Comert, {Salih Vehbi} and Isin Erer",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021 ; Conference date: 15-11-2021 Through 17-11-2021",

year = "2021",

doi = "10.1109/CAMA49227.2021.9703483",

language = "English",

series = "2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "398--401",

booktitle = "2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021",

address = "United States",

}

Comert, SV & Erer, I 2021, Autoregressive Modelling Techniques for Enhanced TWRI. in 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021. 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021, Institute of Electrical and Electronics Engineers Inc., pp. 398-401, 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021, Antibes Juan-les-Pins, France, 15/11/21. https://doi.org/10.1109/CAMA49227.2021.9703483

Autoregressive Modelling Techniques for Enhanced TWRI. / Comert, Salih Vehbi; Erer, Isin.
2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 398-401 (2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Autoregressive Modelling Techniques for Enhanced TWRI

AU - Comert, Salih Vehbi

AU - Erer, Isin

PY - 2021

Y1 - 2021

N2 - High resolution methods such as spectral estimation or compressive sensing based ones have been proposed for through-wall radar (TWR) since conventional beam-forming approach suffers from high side-lobes besides low resolution. In this study auto-regressive (AR) modeling is applied to TWR data after beamforming to enhance image quality. The results are compared with existing beam-space MUSIC and compressive sensing based methods in terms of target detectability.

AB - High resolution methods such as spectral estimation or compressive sensing based ones have been proposed for through-wall radar (TWR) since conventional beam-forming approach suffers from high side-lobes besides low resolution. In this study auto-regressive (AR) modeling is applied to TWR data after beamforming to enhance image quality. The results are compared with existing beam-space MUSIC and compressive sensing based methods in terms of target detectability.

KW - autoregressive modeling

KW - MUSIC spectral estimate

KW - spectral estimation

KW - Through the wall radar

UR - http://www.scopus.com/inward/record.url?scp=85126804337&partnerID=8YFLogxK

U2 - 10.1109/CAMA49227.2021.9703483

DO - 10.1109/CAMA49227.2021.9703483

M3 - Conference contribution

AN - SCOPUS:85126804337

T3 - 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021

SP - 398

EP - 401

BT - 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021

Y2 - 15 November 2021 through 17 November 2021

ER -

Autoregressive Modelling Techniques for Enhanced TWRI

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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