TY - JOUR
T1 - Tracking of signal and its derivatives in Gaussian white noise
AU - Chow, P. L.
AU - Khasminskii, R.
AU - Liptser, R.
N1 - Funding Information:
* Corresponding author. ’ Partially supported by the ONR Grant NO00 14-95-l-0793. * Partially supported by the ONR Grant N00014-96-l-0413.
PY - 1997/9/1
Y1 - 1997/9/1
N2 - For the observation model "signal + white Gaussian noise", an on-line tracking algorithm for signal and its derivatives is proposed. The tracking algorithm applies to a class of signals with derivative up to the kth order. The asymptotic optimality in the minimax sense, with respect to small intensity of noise, is established.
AB - For the observation model "signal + white Gaussian noise", an on-line tracking algorithm for signal and its derivatives is proposed. The tracking algorithm applies to a class of signals with derivative up to the kth order. The asymptotic optimality in the minimax sense, with respect to small intensity of noise, is established.
KW - Gaussian white noise
KW - Ito's equations
KW - Kernel estimator
KW - On-line tracking algorithm
KW - Riccati equation
UR - http://www.scopus.com/inward/record.url?scp=0031234501&partnerID=8YFLogxK
U2 - 10.1016/S0304-4149(97)00046-X
DO - 10.1016/S0304-4149(97)00046-X
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AN - SCOPUS:0031234501
VL - 69
SP - 259
EP - 273
JO - Stochastic Processes and their Applications
JF - Stochastic Processes and their Applications
SN - 0304-4149
IS - 2
ER -