TY - GEN

T1 - Sliding window recursive quadratic optimization with variable regularization

AU - Hoagg, Jesse B.

AU - Ali, Asad A.

AU - Mossberg, Magnus

AU - Bernstein, Dennis S.

PY - 2011

Y1 - 2011

N2 - In this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters. Furthermore, standard recursive least squares uses time-invariant regularization. More specifically, the inverse of the initial covariance matrix in standard recursive least squares can be viewed as a regularization term, which weights the difference between the next estimate and the initial estimate. This regularization is fixed for all steps of the recursion. The algorithm derived in this paper allows for time-varying regularization. In particular, the present paper allows for time-varying regularization in the weighting as well as what is being weighted. Specifically, the regularization term can weight the difference between the next estimate and a time-varying vector of parameters rather than the initial estimate.

AB - In this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters. Furthermore, standard recursive least squares uses time-invariant regularization. More specifically, the inverse of the initial covariance matrix in standard recursive least squares can be viewed as a regularization term, which weights the difference between the next estimate and the initial estimate. This regularization is fixed for all steps of the recursion. The algorithm derived in this paper allows for time-varying regularization. In particular, the present paper allows for time-varying regularization in the weighting as well as what is being weighted. Specifically, the regularization term can weight the difference between the next estimate and a time-varying vector of parameters rather than the initial estimate.

UR - http://www.scopus.com/inward/record.url?scp=80053139317&partnerID=8YFLogxK

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U2 - 10.1109/acc.2011.5991159

DO - 10.1109/acc.2011.5991159

M3 - Conference contribution

AN - SCOPUS:80053139317

SN - 9781457700804

T3 - Proceedings of the American Control Conference

SP - 3275

EP - 3280

BT - Proceedings of the 2011 American Control Conference, ACC 2011

ER -