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. The paper proposes a new algorithm to estimate accurately signals from underdetermined mixtures with fewer 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 nonGaussian noise. This algorithm is also applicable to separating time-varying and instantaneous mixtures. Simulation results demonstrate the efficacy of the proposed algorithm for separation of time-varying mixtures in the presence of noise.