  The SPS-LASSO has recently been introduced as a solution to the problem of
regularization parameter selection in the complex-valued LASSO problem. Still,
the dependence on the grid size and the polynomial time of performing convex
optimization technique in each iteration, in addition to the deficiencies in
the low noise regime, confines its performance for Direction of Arrival (DOA)
estimation. This work presents methods to apply LASSO without grid size
limitation and with less complexity. As we show by simulations, the proposed
methods loose a negligible performance compared to the Maximum Likelihood (ML)
estimator, which needs a combinatorial search We also show by simulations that
compared to practical implementations of ML, the proposed techniques are less
sensitive to the source power difference.
