Criterion for signal extraction in underdetermined mixtures of bounded support

This work studies the problem of recovering a complex signal (source) from an underdetermined linear mixture of bounded sources. We assume some a priori information of the desired signal in the form of a training sequence and complete absence of knowledge from the other sources, except for their bounded character. The main contribution of this letter is the proposal of a bounded component analysis of the training error that tries to condense the relevant information of the observations in a linear estimate of the desired signal. This subspace can be later used for subsequent refined estimation of the signal of interest. Simulations corroborate the good performance of the proposed method in high SNR scenarios.

► We recover a complex signal from an underdetermined mixture of bounded sources. ► We develop a bounded component analysis of the training error. ► The proposed partial zero-forcing criterion has no local minima. ► The strongest interferences are canceled in the resulting linear estimate.