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An adaptive filter using scanning observation with application to a DPS
Author(s) -
Muraca P.,
Pugliese P.
Publication year - 1994
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480080603
Subject(s) - observability , control theory (sociology) , estimator , convergence (economics) , kalman filter , computer science , trajectory , filter (signal processing) , basis (linear algebra) , set (abstract data type) , extended kalman filter , adaptive filter , state (computer science) , mathematics , algorithm , artificial intelligence , computer vision , statistics , physics , geometry , control (management) , astronomy , economics , programming language , economic growth
In this paper an observation strategy is presented, based on the concept of scanning observability, for finite‐dimensional linear systems with discrete observations. It aims to improve the convergence properties of the extended Kalman filter, regarded as an adaptive estimator of the state for the above class of systems. A significant application to a distributed parameter system is shown, related to the problem of determining a convenient trajectory for a moving sensor or a convenient scanning observation rule over a set of fixed sensors. the effectiveness of the resulting filter is analysed on the basis of several numerical experiments.