Gridless GLRT for Tomographic SAR Detection Using Particle Swarm Optimization Algorithm

The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of heigh...

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Published in:	IEEE geoscience and remote sensing letters Vol. 21; pp. 1 - 5
Main Authors:	Haddad, Nabil, Budillon, Alessandra, Hadj-Rabah, Karima, Bouaraba, Azzedine, Harkati, Lekhmissi, Benbouzid, Mohammed Amine, Schirinzi, Gilda
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Computational efficiency Detectors Generalized likelihood ratio test (GLRT) detection Geoscience and remote sensing gridless GLRT Height height estimation Image reconstruction Likelihood ratio Linear programming Minimization Particle swarm optimization particle swarm optimization (PSO) Radar detection Resolution cell Root-mean-square errors SAR (radar) Synthetic aperture radar synthetic aperture radar (SAR) tomography (TomoSAR) Three dimensional models Tomography Vectors
ISSN:	1545-598X, 1558-0571
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Abstract	The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.
AbstractList	The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.
Author	Harkati, Lekhmissi Budillon, Alessandra Bouaraba, Azzedine Haddad, Nabil Hadj-Rabah, Karima Benbouzid, Mohammed Amine Schirinzi, Gilda
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SubjectTerms	Accuracy Algorithms Computational efficiency Detectors Generalized likelihood ratio test (GLRT) detection Geoscience and remote sensing gridless GLRT Height height estimation Image reconstruction Likelihood ratio Linear programming Minimization Particle swarm optimization particle swarm optimization (PSO) Radar detection Resolution cell Root-mean-square errors SAR (radar) Synthetic aperture radar synthetic aperture radar (SAR) tomography (TomoSAR) Three dimensional models Tomography Vectors
Title	Gridless GLRT for Tomographic SAR Detection Using Particle Swarm Optimization Algorithm
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