A METHOD BASED ON L-BFGS TO SOLVE CONSTRAINED COMPLEX-VALUED ICA

Complex-valued independent component analysis (ICA) is a celebrated method in blind separation of complex-valued signals. In this paper, we propose to transform the constrained optimization problems of complex-valued ICA into unconstrained optimization problems which can be solved by limited-memory Broyden-Fletcher-Goldfarb-Shanno update (L-BFGS). As opposed to previous approaches, the proposed method does not apply any restriction on the Hessian matrix of ICA cost function. It can separate mixed sub Gaussian, super-Gaussian, circular, and non-circular sources. Simulations show promising results.