A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator

Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information urban infrastructures. Due to widespread applications multiple signal classification (MUSIC) algorithm in source localization, it a suitable solution TomoSAR when snapshots (looks) are available. While classical MUSIC aims estimate whole reflectivity profile scatterers, sequential algorithms suited detection sparse point-like scatterers. In this class methods, successive cancellation performed through orthogonal complement projections on power spectrum. work, new named recursive covariance cancelled (RCC-MUSIC), proposed. This method brings higher accuracy comparison with previous methods at cost negligible increase computational cost. Furthermore, improve performance RCC-MUSIC, combined recent matrix estimation called correlation subspace. Utilizing subspace results denoised which turn, increases subspace-based methods. Several numerical examples presented compare proposed relevant state-of-the-art As method, simulation demonstrate efficiency terms and load.