Minor subspace tracking using MNS technique

This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind system identification. We exploit here the principle of the MNS, to derive the most efficient algorithms for MST. The proposed method joins the advantages of low complexity and fast convergence rate. Moreover, this method is highly parallelizable and hence its computational cost can be easily reduced to a very low level when parallel architectures are available. Different implementations are proposed for different contexts and they are compared via numerical simulations.