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Online Sequential Compressed Sensing with Multiple Information for Through-the-Wall Radar Imaging. / Becquaert, Mathias; Cristofani, Edison; Lauwens, Ben; Marijke, Vandewal; Stiens, Johan; Deligiannis, Nikolaos.

In: IEEE Sensors Journal, Vol. 19, No. 11, 8637951, 2019, p. 4138-4148.

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Becquaert, Mathias ; Cristofani, Edison ; Lauwens, Ben ; Marijke, Vandewal ; Stiens, Johan ; Deligiannis, Nikolaos. / Online Sequential Compressed Sensing with Multiple Information for Through-the-Wall Radar Imaging. In: IEEE Sensors Journal. 2019 ; Vol. 19, No. 11. pp. 4138-4148.

BibTeX

@article{6483d7911c454c1896a14e4e8ad557ad,
title = "Online Sequential Compressed Sensing with Multiple Information for Through-the-Wall Radar Imaging",
abstract = "We propose a novel strategy for applying compressed sensing (CS) to stepped frequency continuous wave synthetic aperture radars. The measurements are performed by adhering to a sequential measurement strategy. The sensor autonomously adapts, on the fly, the number of samples needed to reconstruct the reflectivity function and guarantees an imposed reconstruction quality. The measurements obtained at the previous scanning positions are added as side information into the reconstruction of the reflectivity function sensed by the radar at the current radar position. The algorithm attributes autonomous weights to each of these side information depending on the similarity with the signal to reconstruct. This approach is tested and evaluated on a series of simulated and real through-the-wall imaging radar measurements for detecting static human targets hidden behind a wall. The experiments first prove that the frequency sampling rate can be decreased far below the bound obtained by the common CS approach and, second, that the algorithm allows determining an accurate upper bound for the reconstruction error, and thus to autonomously decide online on the number of samples.",
keywords = "Compressed sensing, side information, synthetic aperture radar, through-the-wall imaging",
author = "Mathias Becquaert and Edison Cristofani and Ben Lauwens and Vandewal Marijke and Johan Stiens and Nikolaos Deligiannis",
year = "2019",
doi = "10.1109/JSEN.2019.2898274",
language = "English",
volume = "19",
pages = "4138--4148",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - Online Sequential Compressed Sensing with Multiple Information for Through-the-Wall Radar Imaging

AU - Becquaert, Mathias

AU - Cristofani, Edison

AU - Lauwens, Ben

AU - Marijke, Vandewal

AU - Stiens, Johan

AU - Deligiannis, Nikolaos

PY - 2019

Y1 - 2019

N2 - We propose a novel strategy for applying compressed sensing (CS) to stepped frequency continuous wave synthetic aperture radars. The measurements are performed by adhering to a sequential measurement strategy. The sensor autonomously adapts, on the fly, the number of samples needed to reconstruct the reflectivity function and guarantees an imposed reconstruction quality. The measurements obtained at the previous scanning positions are added as side information into the reconstruction of the reflectivity function sensed by the radar at the current radar position. The algorithm attributes autonomous weights to each of these side information depending on the similarity with the signal to reconstruct. This approach is tested and evaluated on a series of simulated and real through-the-wall imaging radar measurements for detecting static human targets hidden behind a wall. The experiments first prove that the frequency sampling rate can be decreased far below the bound obtained by the common CS approach and, second, that the algorithm allows determining an accurate upper bound for the reconstruction error, and thus to autonomously decide online on the number of samples.

AB - We propose a novel strategy for applying compressed sensing (CS) to stepped frequency continuous wave synthetic aperture radars. The measurements are performed by adhering to a sequential measurement strategy. The sensor autonomously adapts, on the fly, the number of samples needed to reconstruct the reflectivity function and guarantees an imposed reconstruction quality. The measurements obtained at the previous scanning positions are added as side information into the reconstruction of the reflectivity function sensed by the radar at the current radar position. The algorithm attributes autonomous weights to each of these side information depending on the similarity with the signal to reconstruct. This approach is tested and evaluated on a series of simulated and real through-the-wall imaging radar measurements for detecting static human targets hidden behind a wall. The experiments first prove that the frequency sampling rate can be decreased far below the bound obtained by the common CS approach and, second, that the algorithm allows determining an accurate upper bound for the reconstruction error, and thus to autonomously decide online on the number of samples.

KW - Compressed sensing

KW - side information

KW - synthetic aperture radar

KW - through-the-wall imaging

UR - http://www.scopus.com/inward/record.url?scp=85065404852&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2019.2898274

DO - 10.1109/JSEN.2019.2898274

M3 - Article

VL - 19

SP - 4138

EP - 4148

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 11

M1 - 8637951

ER -

ID: 45429421