  Dynamic tracking of sparse targets has been one of the important topics in
array signal processing. Recently, compressed sensing (CS) approaches have been
extensively investigated as a new tool for this problem using partial support
information obtained by exploiting temporal redundancy. However, most of these
approaches are formulated under single measurement vector compressed sensing
(SMV-CS) framework, where the performance guarantees are only in a
probabilistic manner. The main contribution of this paper is to allow
\textit{deterministic} tracking of time varying supports with multiple
measurement vectors (MMV) by exploiting multi-sensor diversity. In particular,
we show that a novel compressive MUSIC (CS-MUSIC) algorithm with optimized
partial support selection not only allows removal of inaccurate portion of
previous support estimation but also enables addition of newly emerged part of
unknown support. Numerical results confirm the theory.
