  This paper addresses the general problem of single-look multi-master SAR
tomography. For this purpose, we establish the single-look multi-master data
model, analyze its implications for single and double scatterers, and propose a
generic inversion framework. The core of this framework is nonconvex sparse
recovery, for which we develop two algorithms: one extends the conventional
nonlinear least squares (NLS) to the single-look multi-master data model, and
the other is based on bi-convex relaxation and alternating minimization
(BiCRAM). We provide two theorems for the objective function of the NLS
subproblem, which lead to its analytic solution up to a constant phase angle in
the one-dimensional case. We also report our findings from the experiments on
different acceleration techniques for BiCRAM. The proposed algorithms are
applied to a real TerraSAR-X data set, and validated with height ground truth
made available via a SAR imaging geodesy and simulation framework. This shows
empirically that the \emph{single-master} approach, if applied to a single-look
\emph{multi-master} stack, can be insufficient for layover separation, and the
\emph{multi-master} approach can indeed perform slightly better (despite being
computationally more expensive) even in the case of single scatterers. Besides,
this paper also sheds light on the special case of single-look bistatic SAR
tomography, which is relevant for current and future SAR missions such as
TanDEM-X and Tandem-L.
