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

In sparse DOA estimation methods, the regularization parameter is generally empirically tuned. In this paper, we provide a statistical method allowing to estimate an admissible interval where it must be chosen. This work is conducted in the case of an Uniform Circular Array, well known for its angle-invariant performances, and vectorized covariance matrix observation. In a recent work by Nikolova, it is shown that the equivalence between the L0-constrained problem and the corresponding regularized one is obtained for the regularization parameter 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 for the parameter valid in all scenarios for an UCA. This interval is not data dependent. Simulation results validate the proposed approach.