On Regularization Parameter for L0-Sparse Covariance Fitting Based DOA Estimation
Alice Delmer, Anne Ferréol, Pascal Larzabal
Abstract
In sparse DOA estimation methods, the regularization parameter λ is generally empirically tuned. In this paper, we provide a statistical method allowing to estimate an admissible interval where λ must be chosen. This work is conducted in the case of an Uniform Circular Array, well known for its θ invariant performances, and vectorized covariance matrix observation. In the recent work [1], it is shown that the equivalence between the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -constrained problem and the corresponding regularized one is obtained for λ 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 λ valid in all scenarios for an UCA. This interval is not data dependent. Simulation results validate the proposed approach.
BibTeX
@inproceedings{icassp2020_onregularization,
title = {On Regularization Parameter for L0-Sparse Covariance Fitting Based DOA Estimation},
author = {Alice Delmer and Anne Ferréol and Pascal Larzabal},
booktitle = {ICASSP 2020},
year = {2020}
}