TY - GEN
T1 - Dynamic estimation with selectable linear measurements
AU - Sinno, Dana
AU - Cochran, Douglas
PY - 1998
Y1 - 1998
N2 - This paper is concerned with a class of dynamic estimation problems in which the estimator has the ability to dynamically select, from among a temporally evolving set of possibilities, the source of the data on which the estimate will be based. After motivating and formulating this class of attentive estimation problems in some generality, the paper focuses on the special case in which the state of a linear discrete-time dynamical system driven by gaussian noise is to be estimated using linear measurements corrupted by additive gaussian noise. This differs from the standard Kalman filtering problem in that the measurement map at each time step is selectable from a pre-determined set of such maps. When the system dynamics and noise statistics are known, the problem admits a sensor scheduling solution i.e., a criterion for measurement selection that can be used to determine an optimal sequence of output functions in an open-loop fashion prior to the onset of estimation. When the noise statistics or other parameters are unknown, however, closed-loop adaptive strategies for measurement selection can improve estimator performance.
AB - This paper is concerned with a class of dynamic estimation problems in which the estimator has the ability to dynamically select, from among a temporally evolving set of possibilities, the source of the data on which the estimate will be based. After motivating and formulating this class of attentive estimation problems in some generality, the paper focuses on the special case in which the state of a linear discrete-time dynamical system driven by gaussian noise is to be estimated using linear measurements corrupted by additive gaussian noise. This differs from the standard Kalman filtering problem in that the measurement map at each time step is selectable from a pre-determined set of such maps. When the system dynamics and noise statistics are known, the problem admits a sensor scheduling solution i.e., a criterion for measurement selection that can be used to determine an optimal sequence of output functions in an open-loop fashion prior to the onset of estimation. When the noise statistics or other parameters are unknown, however, closed-loop adaptive strategies for measurement selection can improve estimator performance.
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U2 - 10.1109/ICASSP.1998.681582
DO - 10.1109/ICASSP.1998.681582
M3 - Conference contribution
AN - SCOPUS:0031623690
SN - 0780344286
SN - 9780780344280
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2193
EP - 2196
BT - Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
T2 - 1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Y2 - 12 May 1998 through 15 May 1998
ER -