Generalized MNS method for parallel minor and principal subspace analysis

This paper introduces a generalized minimum noise subspace method for the fast estimation of the minor or principal subspaces for large dimensional multi-sensor systems. In particular, the proposed method allows parallel computation of the desired subspace when K > 1 computational units (DSPs) are available in a parallel architecture. The overall numerical cost is approximately reduced by a factor of K
²
while preserving the estimation accuracy close to optimality. Different algorithm implementations are considered and their performance is assessed through numerical simulation.