Abstract
The standard $H_2$ optimal filtering problem considers the estimation of a certain output based on the measured output when the input is a zero mean white noise stochastic process of known intensity. In this paper, the inputs are considered to be of two types. The first type of input, as in standard $H_2$ optimal filtering, is a zero mean wide sense stationary white noise, while the second type is a linear combination of sinusoidal signals each of which has an unknown amplitude and phase but known frequency. Under such inputs, a generalized $H_2$ optimal filtering problem is formulated here. As in the standard $H_2$ optimal filtering problem, the generalized $H_2$ optimal filtering problem seeks to find a linear stable unbiased filter (called the generalized $H_2$ optimal filter) that estimates a desired output while utilizing the measured output such that the $H_2$ norm of the transfer matrix from the white noise input to the estimation error is minimized. The analysis, design, and performance limitations of generalized $H_2$ optimal filters are presented here.
Original language | English |
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Pages | 3454-3459 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2004 |
Event | 2004 American Control Conference, ACC 2004 - Boston, United States Duration: 30 Jun 2004 → 2 Jul 2004 |
Conference
Conference | 2004 American Control Conference, ACC 2004 |
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Abbreviated title | ACC |
Country/Territory | United States |
City | Boston |
Period | 30/06/04 → 2/07/04 |