Low complexity sparse Bayesian estimation for UWB radar imaging

Ultra-wideband (UWB) radar imaging can provide high-resolution images of obscured objects using radio-frequency signals. Due to its vast applications, UWB radar imaging received considerable attention in the past decade. Compressive sensing (CS) has been used as a viable solution for the larger data...

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Bibliographic Details
Published in:IET radar, sonar & navigation Vol. 12; no. 2; pp. 268 - 275
Main Authors: Alkhodary, Mohammad T, Muqaibel, Ali H
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.02.2018
Subjects:
basis‐matrix
block‐matrix‐inversion formula
compressed sensing
compressive sensing
computational complexity
CS‐based UWB‐radar imaging
exponential‐sum formula
high‐resolution images
image reconstruction
image resolution
low‐complexity Bayesian‐based estimation algorithm
low‐complexity sparse Bayesian estimation
matrix inversion
mean square error methods
minimum mean‐square error estimate
noise immunity
obscured objects
order‐recursive manner
radar imaging
radar‐return statistic
radiofrequency signals
reconstruction algorithms
reconstruction quality
Research Article
ultra wideband radar
ultrawideband radar imaging
compressed sensing
radar-return statistic
CS-based UWB-radar imaging
reconstruction quality
obscured objects
basis-matrix
noise immunity
radar imaging
mean square error methods
image resolution
ultra wideband radar
matrix inversion
image reconstruction
low-complexity Bayesian-based estimation algorithm
block-matrix-inversion formula
minimum mean-square error estimate
reconstruction algorithms
ultrawideband radar imaging
exponential-sum formula
order-recursive manner
high-resolution images
radiofrequency signals
compressive sensing
computational complexity
low-complexity sparse Bayesian estimation
ISSN:1751-8784, 1751-8792
Online Access:Get full text
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Description
Summary:Ultra-wideband (UWB) radar imaging can provide high-resolution images of obscured objects using radio-frequency signals. Due to its vast applications, UWB radar imaging received considerable attention in the past decade. Compressive sensing (CS) has been used as a viable solution for the larger data required by radar imaging. The advances of CS-based UWB-radar imaging is burdened by the complexity of the reconstruction algorithms and their weak noise immunity. Exploiting the structure of the basis-matrix, a low-complexity Bayesian-based estimation algorithm is proposed. The algorithm takes advantage of the radar-return statistic to find an approximate minimum mean-square error estimate of the radar image. The low complexity is achieved by utilising the block-matrix-inversion formula to execute the algorithm in an order-recursive manner. Further simplification is achieved by using exponential-sum formula to find the correlation between the columns of the basis-matrix. The proposed algorithm is evaluated over experimental and simulated data. The results show faster processing time compared to other known algorithms, with comparable reconstruction quality.
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2017.0143