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Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool forthe extraction of height information of urban infrastructures. Due to thewidespread applications of the MUSIC algorithm in source localization, it is asuitable solution in TomoSAR when multiple snapshots (looks) are available.While the classical MUSIC algorithm aims to estimate the whole reflectivityprofile of scatterers, sequential MUSIC algorithms are suited for the detectionof sparse point-like scatterers. In this class of methods, successivecancellation is performed through orthogonal complement projections on theMUSIC power spectrum. In this work, a new sequential MUSIC algorithm namedrecursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This methodbrings higher accuracy in comparison with the previous sequential methods atthe cost of a negligible increase in computational cost. Furthermore, toimprove the performance of RCC-MUSIC, it is combined with the recent method ofcovariance matrix estimation called correlation subspace. Utilizing thecorrelation subspace method results in a denoised covariance matrix which inturn, increases the accuracy of subspace-based methods. Several numericalexamples are presented to compare the performance of the proposed method withthe relevant state-of-the-art methods. As a subspace method, simulation resultsdemonstrate the efficiency of the proposed method in terms of estimationaccuracy and computational load.

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator