Non-sparse approach to underdetermined blind signal estimation

Conventional assumptions of square mixing matrix and negligible noise adopted in blind signal separation do not always correspond with real applications. Signal detection from a small number of sensors is often required in signal and image modeling and biomedical applications. This paper proposes a new algorithm to accurately estimate signals from underdetermined mixtures with less restrictions and assumptions compared with existing techniques. The strength of this algorithm is that it does not adopt the conventional assumptions on the mixing, signals and noise. The algorithm is capable of separating orthogonal and non-orthogonal mixtures of both sparse and non-sparse signals with additional Gaussian or non-Gaussian noise. This algorithm is also applicable to separating time-varying as well as instantaneous mixtures. Simulation results demonstrate the efficacy of the proposed algorithm for separation of time-varying mixtures in the presence of noise.