A parallelizable recursive least squares algorithm for adaptive filtering, with very good tracking properties

In this paper, a new Recursive Least Squares(RLS)algorithm for Finite Window Adaptive Filtering is presented, that has a number of interesting and useful properties. First, owing to the specific structure of the updating formulas and due to the fact that the past information is, for the first time,...

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Vydáno v:International journal of computer mathematics Ročník 67; číslo 3-4; s. 275 - 292
Hlavní autoři: Papaodysseus, C., Koukoutsis, E., Halkias, C.C., Roussopoulos, G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Gordon and Breach Science Publishers 01.01.1998
Taylor and Francis
Témata:
Adaptive algorithms
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
I4.4
kalman-type parallel algorithms and filters
Pattern recognition. Digital image processing. Computational geometry
recursive least squares(RLS)filtering
Theoretical computing
Image analysis
Adaptive algorithm
Least squares method
Kalman filter
Algorithm complexity
Adaptive filtering
Image restoration
Recursive algorithm
Computational complexity
ISSN:0020-7160, 1029-0265
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Popis
Shrnutí:In this paper, a new Recursive Least Squares(RLS)algorithm for Finite Window Adaptive Filtering is presented, that has a number of interesting and useful properties. First, owing to the specific structure of the updating formulas and due to the fact that the past information is, for the first time, directly dropped by means of a proper inversion Lemma stated and proved in this paper, the proposed algorithm is immediately parallelizable. Second, it is more robust than many RLS Kalman-type schemes, in the sense that it is more resistant to the finite precision error effects. At the same time, the proposed algorithm has very good tracking capabilities. Finally, it can constitute the basis for the development of 0(m)computational complexity algorithms that have very interesting properties, too, i.e. they are robust, parallelizable and they have particularly good tracking properties.
Bibliografie:ObjectType-Article-2
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ISSN:0020-7160
1029-0265
DOI:10.1080/00207169808804665