Title :
New fast QR decomposition least squares adaptive algorithms
Author :
Rontogiannis, Athanasios A. ; Theodoridis, Sergios
Author_Institution :
Dept. of Inf., Athens Univ., Greece
fDate :
8/1/1998 12:00:00 AM
Abstract :
This paper presents two new, closely related adaptive algorithms for LS system identification. The starting point for the derivation of the algorithms is the inverse Cholesky factor of the data correlation matrix, obtained via a QR decomposition (QRD). Both algorithms are of O(p) computational complexity, with p being the order of the system. The first algorithm is a fixed order QRD scheme with enhanced parallelism. The second is an order recursive lattice type algorithm based exclusively on orthogonal Givens rotations, with lower complexity compared to previously derived ones. Both algorithms are derived following a new approach, which exploits efficient the and order updates of a specific state vector quantity
Keywords :
adaptive signal processing; computational complexity; convergence of numerical methods; identification; least squares approximations; matrix inversion; parallel algorithms; recursive estimation; state-space methods; transient response; FIR system; LS system identification; computational complexity; convergence; data correlation matrix; enhanced parallelism; fast QR decomposition; fixed order QRD; inverse Cholesky factor; inverse input data matrix; least squares adaptive algorithms; order recursive lattice type algorithm; order updates; orthogonal Givens rotations; state vector; Adaptive algorithm; Computational complexity; Finite impulse response filter; Lattices; Least squares methods; Matrix decomposition; Parallel processing; Robustness; Signal processing algorithms; System identification;
Journal_Title :
Signal Processing, IEEE Transactions on
