Title :
STAR recursive least square lattice adaptive filters
Author :
Li, Yuet ; Parhi, Keshab K.
Author_Institution :
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA
fDate :
12/1/1997 12:00:00 AM
Abstract :
The recursive least square lattice (LSL) algorithm based on the newly developed scaled tangent rotations (STAR) is derived. Similar to other recursive least square lattice algorithms for adaptive filtering, this algorithm requires only O(N) operations. This algorithm also preserves the desired properties of the STAR recursive least square (STAR-RLS) algorithm. Specifically, it can be pipelined at fine-grain level. To this end, a pipelined version of the STAR-LSL (referred to as PSTAR-LSL) is also developed. Computer simulations show that the performance of the STAR-LSL algorithm is as good as the QRD-LSL algorithm. The finite precision error properties of the STAR-LSL algorithm are also analyzed. The mean square error expressions show that the numerical error propagates from stage to stage in the lattice, and the numerical error of different quantities in the algorithm varies differently with λ. This suggests that different word lengths need to be assigned to different variables in the algorithm for best performance. Finally, finite word length simulations are carried out to compare the performances of different topologies
Keywords :
adaptive filters; error analysis; filtering theory; lattice filters; least squares approximations; matrix algebra; pipeline processing; prediction theory; recursive filters; RLS filtering; STAR recursive adaptive filters; backward prediction; fine-grain level pipelining; finite precision error properties; finite word length simulations; hardware implementation; least square lattice adaptive filters; mean square error expressions; numerical error; pipelined version; recursive least square lattice algorithm; scaled tangent rotations; Adaptive filters; Computer errors; Convergence; Discrete Fourier transforms; Discrete wavelet transforms; Lattices; Least squares approximation; Least squares methods; Magnetic noise; Resonance light scattering;
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on