An instrumental variable based subspace tracking algorithm based on subspace averaging
Tony Gustafsson · 2002
In this paper an instrumental variable based subspace tracking algorithm is proposed. The basic idea of the algorithm is to reduce the amount of computations using a certain perturbation/approximation strategy. The complexity is reduced to O(mn/sup 2/), which should be compared to O(ml/sup 2/) for the SVD, where m, l/spl Gt/n in general (m denotes the number of sensors, l denotes the number of instruments, and n denotes the number of signals). The proposed algorithm turns out to be related to Karasalo's subspace averaging approach (1986). In a series of simulations we demonstrate that the detection, stationary estimation, and tracking performance of the proposed algorithm is essentially equivalent to that achieved by the truncated SVD.