  Layover separation has been fundamental to many synthetic aperture radar
applications, such as building reconstruction and biomass estimation.
Retrieving the scattering profile along the mixed dimension (elevation) is
typically solved by inversion of the SAR imaging model, a process known as SAR
tomography. This paper proposes a nonlinear blind scatterer separation method
to retrieve the phase centers of the layovered scatterers, avoiding the
computationally expensive tomographic inversion. We demonstrate that
conventional linear separation methods, e.g., principle component analysis
(PCA), can only partially separate the scatterers under good conditions. These
methods produce systematic phase bias in the retrieved scatterers due to the
nonorthogonality of the scatterers' steering vectors, especially when the
intensities of the sources are similar or the number of images is low. The
proposed method artificially increases the dimensionality of the data using
kernel PCA, hence mitigating the aforementioned limitations. In the processing,
the proposed method sequentially deflates the covariance matrix using the
estimate of the brightest scatterer from kernel PCA. Simulations demonstrate
the superior performance of the proposed method over conventional PCA-based
methods in various respects. Experiments using TerraSAR-X data show an
improvement in height reconstruction accuracy by a factor of one to three,
depending on the used number of looks.
