Reduced-order estimation Part 1. Filtering

Reduced Order Kalman Filtering Without Model Reduction

“…This paper does not make any approximations in the order of the model and does not assume any special structure in the system dynamics. This is similar to the approaches proposed by Sims [17], Bernstein [18], Nagpal [19], and Keller [20,21]. Sims' approach involves the solution of a two point boundary value problem, which can be numerically difficult.…”

“…Nagpal [19] and Keller [20,21] present approaches that optimize the transition matrix of the filter as well as the gain of the filter. As our approach does not optimize the fil-173 ter's transition matrix, it is expected that their approaches should yield better results than our approach.…”

Robust and Reduced Order H-Infinity Filtering via LMI Approach and Its Application to Fault Detection

“…The reduced-order 2 H filtering problem was studied in (Nagpal et al, 1987), and the reduced-order H  filtering problem for discrete time systems was studied in (O'Brien & Kiriakidis, 2004), but in this section we will develop an H  filtering approach in LMI framework that is applicable and easy to compute. Let's reconsider (18).…”

Optimal unbiased functional filtering in the frequency domain

“…In this paper we consider the reduced-order filtering problem presented and treated in Nagpal, Helmick, and Sims (1987), Nakamizo (1997), and recently in Darouach (2000), in the time domain. Starting from the recent results on the optimal unbiased functional filtering (Darouach, 2000), we investigate the frequency domain approach.…”