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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Vydáno v:	IET radar, sonar & navigation Ročník 12; číslo 2; s. 268 - 275
Hlavní autoři:	Alkhodary, Mohammad T, Muqaibel, Ali H
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.02.2018
Témata:	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
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Shrnutí:	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