Title :
A systolic array for recursive least squares computations: mapping directionally weighted RLS on an SVD updating array
Author_Institution :
ESAT, Katholieke Univ., Leuven, Heverlee, Belgium
fDate :
8/1/1996 12:00:00 AM
Abstract :
A systolic algorithm/array is described for recursive least squares (RLS) estimation, which achieves an O(n0) throughput rate with O(n2) parallelism. The array is also useful for several other applications, such as, e.g., SVD updating and Kalman filtering. An additional advantage is that unlike with other RLS-arrays, it is now possible to incorporate alternative data weighting strategies, such as directional weighting, without sacrificing speed
Keywords :
Kalman filters; filtering theory; least squares approximations; parallel algorithms; recursive estimation; singular value decomposition; systolic arrays; Kalman filtering; SVD updating array; data weighting; directionally weighted RLS; parallelism; recursive least squares computations; recursive least squares estimation; systolic algorithm; systolic array; throughput rate; Concurrent computing; Filtering; Kalman filters; Least squares approximation; Least squares methods; Parallel processing; Resonance light scattering; Signal processing algorithms; Systolic arrays; Throughput;
Journal_Title :
Signal Processing, IEEE Transactions on
