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