Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Identification of Time-Varying Processes
Identification of Time-Varying Processes
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Tracking analysis of a generalized adaptive notch filter
IEEE Transactions on Signal Processing
Posterior Cramer-Rao bound for adaptive harmonic retrieval
IEEE Transactions on Signal Processing
Filtering, predictive, and smoothing Cramér-Rao bounds for discrete-time nonlinear dynamic systems
Automatica (Journal of IFAC)
Can the zero-lag filter be a good smoother?
IEEE Transactions on Information Theory
On "cheap smoothing" opportunities in identification of time-varying systems
Automatica (Journal of IFAC)
Generalized adaptive notch smoothing revisited
IEEE Transactions on Signal Processing
| Hi-index | 22.15 |
In certain applications of nonstationary system identification the model-based decisions can be postponed, i.e. executed with a delay. This allows one to incorporate in the identification process not only the currently available information, but also a number of ''future'' data points. The resulting estimation schemes, which involve smoothing, are not causal. Assuming that the infinite observation history is available, the paper establishes the lower steady-state estimation bound for any noncausal estimator applied to a linear system with randomly drifting coefficients (under Gaussian assumptions). This lower bound complements the currently available one, which is restricted to causal estimators.