  Sparse sensor placement, with various design objectives, has successfully
been employed in diverse application areas, particularly for enhanced parameter
estimation and receiver performance. The sparse array design criteria are
generally categorized into environment-independent and environment-dependent
performance metrics. The former are largely benign to the underlying
environment and, in principle, seek to maximize the spatial degrees of freedom
by extending the coarray aperture. Environment-dependent objectives, on the
other hand, consider the operating conditions characterized by emitters and
targets in the array field of view, in addition to receiver noise. In this
regard, applying such objectives renders the array configuration as well as the
array weights time-varying in response to dynamic and changing environment.
  This work is geared towards designing environment-dependent sparse array
beamformer to improve the output signal-to-interference and noise ratio using
both narrowband and wideband signal platforms. One key challenge in
implementing the data-dependent approaches is the lack of knowledge of exact or
estimated values of the data autocorrelation function across the full sparse
array aperture. At the core of this work is to address the aforementioned
issues by devising innovative solutions using convex optimization and machine
learning tools, structured sparsity concepts, low rank matrix completion
schemes and fusing the environment-dependent and environment-independent deigns
by developing a hybrid approach.
