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

Department of Electronics and Communication Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	398-401
Number of pages	4
ISBN (Electronic)	9781728196978
DOIs	https://doi.org/10.1109/CAMA49227.2021.9703483
Publication status	Published - 2021
Event	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021 - Antibes Juan-les-Pins, France Duration: 15 Nov 2021 → 17 Nov 2021

Publication series

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

Conference

Conference	2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
Country/Territory	France
City	Antibes Juan-les-Pins
Period	15/11/21 → 17/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

autoregressive modeling
MUSIC spectral estimate
spectral estimation
Through the wall radar

Access to Document

10.1109/CAMA49227.2021.9703483

Cite this

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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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 - https://www.scopus.com/pages/publications/85126804337

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

Abstract

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Bibliographical note

Keywords

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