Correction to "Bi-iteration recursive instrumental variable subspace tracking and adaptive filtering"

Bi-iteration recursive instrumental variable subspace tracking and adaptive filtering

In this paper, we propose a class of fast sequential bi-iteration singular value (Bi-SVD) subspace tracking algorithms for adaptive eigendecomposition of the cross covariance matrix in the recursive instrumental variable (RIV) method of system identification. These algorithms can be used for RIV subspace processing of signals in unknown correlated Gaussian noise. Realizations with O(Nr) and O(N...

Robust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filtering

Recursive subspace identification based on instrumental variable unconstrained quadratic optimization

The problem of the recursive formulation of the MOESP class of subspace identification algorithms is considered and two novel instrumental variable approaches are introduced. The first one leads to an RLS-like implementation, the second to a gradient type iteration. The relative merits of both approaches are analysed and discussed, while simulation results are used to compare their performance ...

Instrumental Variable Subspace Tracking with Applications to SensorArray

Recursive methods for subspace tracking with applications to 'on-line' direction of arrival estimation, have lately drawn considerable interest. In this paper, Instrumental Variable (IV) generalizations of the Projection Approximation Subspace Tracking (PAST) algorithm are proposed. The IV-approach is motivated by the fact that PAST delivers biased estimates when the noise vectors are not spati...

Recursive System Identification

In this paper a recursive instrumental variable (IV) based subspace identiication algorithm is proposed. The basic idea of the algorithm is to utilize the close relationship with sensor array signal processing. Utilizing this relationship, an IV based subspace tracking algorithm originally developed for direction of arrival tracking is applied to track the subspace spanned by the observability ...