  In this work, we extend the sparse iterative covariance-based estimator
(SPICE), by generalizing the formulation to allow for different norm
constraints on the signal and noise parameters in the covariance model. For a
given norm, the resulting extended SPICE method enjoys the same benefits as the
regular SPICE method, including being hyper-parameter free, although the choice
of norms are shown to govern the sparsity in the resulting solution.
Furthermore, we show that solving the extended SPICE method is equivalent to
solving a penalized regression problem, which provides an alternative
interpretation of the proposed method and a deeper insight on the differences
in sparsity between the extended and the original SPICE formulation. We examine
the performance of the method for different choices of norms, and compare the
results to the original SPICE method, showing the benefits of using the
extended formulation. We also provide two ways of solving the extended SPICE
method; one grid-based method, for which an efficient implementation is given,
and a gridless method for the sinusoidal case, which results in a semi-definite
programming problem.
