  Spectrum cartography aims at estimating power propagation patterns over a
geographical region across multiple frequency bands (i.e., a radio map)---from
limited samples taken sparsely over the region. Classic cartography methods are
mostly concerned with recovering the aggregate radio frequency (RF) information
while ignoring the constituents of the radio map---but fine-grained
emitter-level RF information is of great interest. In addition, many existing
cartography methods work explicitly or implicitly assume random spatial
sampling schemes that may be difficult to implement, due to
legal/privacy/security issues. The theoretical aspects (e.g., identifiability
of the radio map) of many existing methods are also unclear. In this work, we
propose a joint radio map recovery and disaggregation method that is based on
coupled block-term tensor decomposition. Our method guarantees identifiability
of the individual radio map of \textit{each emitter} (thereby that of the
aggregate radio map as well), under realistic conditions. The identifiability
result holds under a large variety of geographical sampling patterns, including
a number of pragmatic systematic sampling strategies. We also propose effective
optimization algorithms to carry out the formulated radio map disaggregation
problems. Extensive simulations are employed to showcase the effectiveness of
the proposed approach.
