On Regularization Parameter For L0-Sparse Covariance Fitting Based Doa Estimation

In sparse DOA estimation methods, the regularization parameter lambda is generally empirically tuned. In this paper, we provide a statistical method allowing to estimate an admissible interval where lambda must be chosen. This work is conducted in the case of an Uniform Circular Array, well known for its theta invariant performances, and vectorized covariance matrix observation. In the recent work [1], it is shown that the equivalence between the l(0)-constrained problem and the corresponding regularized one is obtained for lambda belonging to a given interval. This interval is conditional to an observation. The purpose of this work is to generalize this result for stochastic observations, providing so an interval I of lambda valid in all scenarios for an UCA. This interval is not data dependent. Simulation results validate the proposed approach.