@article {525,
	title = {Computationally efficient steady-state multiscale estimation for 1-D diffusion processes},
	journal = {Automatica},
	volume = {37},
	year = {2001},
	pages = {325 - 340},
	abstract = {<p>Conventional optimal estimation algorithms for distributed parameter systems have been limited due to their computational complexity. In this paper, we consider an alternative modeling framework recently developed for large-scale static estimation problems and extend this methodology to dynamic estimation. Rather than propagate estimation error statistics in conventional recursive estimation algorithms, we propagate a more compact multiscale model for the errors. In the context of 1-D diffusion which we use to illustrate the development of our algorithm, for a discretespace process of N points the resulting multiscale estimator achieves O(N log N) computational complexity (per time step) with near-optimal performance as compared to the O(N) complexity of the standard Kalman filter.</p>
},
	keywords = {diffusion, distributed parameter systems, dynamic estimation, multiscale realization},
	doi = {http://dx.doi.org/10.1016/S0005-1098(00)00174-6},
	author = {T T. Ho and P Fieguth and A S. Willsky}
}
