  Sparse wideband sensor array design for sensor location optimisation is
highly nonlinear and it is traditionally solved by genetic algorithms,
simulated annealing or other similar optimization methods. However, this is an
extremely time-consuming process and more efficient solutions are needed. In
this work, this problem is studied from the viewpoint of compressive sensing
and a formulation based on a modified $l_1$ norm is derived. As there are
multiple coefficients associated with each sensor, the key is to make sure that
these coefficients are simultaneously minimized in order to discard the
corresponding sensor locations. Design examples are provided to verify the
effectiveness of the proposed methods.
