Blind source separation using the second derivative of the second characteristic function

A new algorithm for blind source separation is presented, which does not require any iterations with the raw data, and is therefore of a “closed-form” type. The algorithm is based on estimating the second-derivative matrices of the second joint characteristic function of the observations. These derivatives can be consistently estimated at various points, termed “processing points”. A consistent estimate of the mixing matrix can in turn be obtained by applying approximate joint diagonalization to the estimated derivative matrices. Performance depends strongly on the choice of processing points, and can compare favorably to other BSS algorithms. We demonstrate the superior performance using simulations results