TY - GEN
T1 - Growing window recursive quadratic optimization with variable regularization
AU - Ali, Asad A.
AU - Hoagg, Jesse B.
AU - Mossberg, Magnus
AU - Bernstein, Dennis S.
PY - 2010
Y1 - 2010
N2 - We present a growing-window variable-regularization recursive least squares (GW-VR-RLS) algorithm. Standard recursive least squares (RLS) uses a time-invariant regularization. More specifically, the inverse of the initial covariance matrix in classical RLS can be viewed as a regularization term, which weights the difference between the next state estimate and the initial state estimate. The present paper allows for time-varying in the weighting as well as what is being weighted. This extension can be used to modulate the speed of convergence of the estimates versus the magnitude of transient estimation errors. Furthermore, the regularization term can weight the difference between the next state estimate and a time-varying vector of parameters rather than the initial state estimate as is required in standard RLS.
AB - We present a growing-window variable-regularization recursive least squares (GW-VR-RLS) algorithm. Standard recursive least squares (RLS) uses a time-invariant regularization. More specifically, the inverse of the initial covariance matrix in classical RLS can be viewed as a regularization term, which weights the difference between the next state estimate and the initial state estimate. The present paper allows for time-varying in the weighting as well as what is being weighted. This extension can be used to modulate the speed of convergence of the estimates versus the magnitude of transient estimation errors. Furthermore, the regularization term can weight the difference between the next state estimate and a time-varying vector of parameters rather than the initial state estimate as is required in standard RLS.
UR - https://www.scopus.com/pages/publications/79953142512
UR - https://www.scopus.com/inward/citedby.url?scp=79953142512&partnerID=8YFLogxK
U2 - 10.1109/CDC.2010.5717527
DO - 10.1109/CDC.2010.5717527
M3 - Conference contribution
AN - SCOPUS:79953142512
SN - 9781424477456
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 496
EP - 501
BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010
T2 - 49th IEEE Conference on Decision and Control, CDC 2010
Y2 - 15 December 2010 through 17 December 2010
ER -