A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets

Through-the-wall radar (TWR) imaging is an emerging technology that enables detection and localization of targets behind walls. In practical operations, TWR sensing faces several technical difficulties including strong wall clutter and missing data measurements. This paper proposes a sparse Bayesian...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2017-10, Vol.53 (5), p.2485-2501
Main Authors: Van Ha Tang, Son Lam Phung, Fok Hing Chi Tivive, Bouzerdoum, Abdesselam
Format: Article
Language:English
Subjects:
Antenna measurements
Antennas
Bayes methods
Bayesian analysis
Clutter
Compressed sensing (CS)
Computer simulation
Frequency measurement
Image reconstruction
indoor scene reconstruction
Localization
Machine learning
Missing data
Position (location)
Probabilistic inference
Radar imaging
sparse Bayesian learning (SBL)
sparse representation
Target detection
Target recognition
through-the-wall radar imaging (TWRI)
wall-clutter mitigation
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Summary:Through-the-wall radar (TWR) imaging is an emerging technology that enables detection and localization of targets behind walls. In practical operations, TWR sensing faces several technical difficulties including strong wall clutter and missing data measurements. This paper proposes a sparse Bayesian learning (SBL) approach for wall-clutter mitigation and scene reconstruction from compressed data measurements. In the proposed approach, SBL is used to model both the intraantenna signal sparsity and interantenna signal correlation for estimating the antenna signals jointly. Here, the Bayesian framework provides a learning paradigm for sharing measurements among spatial positions, leading to accurate and stable antenna signal estimation. Furthermore, the task of wall-clutter mitigation is formulated as a probabilistic inference problem, where the wall-clutter subspace and its dimension are learned automatically using the mechanism of automatic relevant determination. Automatic discrimination between targets and clutter allows an effective target image formation, which is performed using Bayesian approximation. Experimental results with both real and simulated TWR data demonstrate the effectiveness of the SBL approach in indoor target detection and localization.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2017.2701646