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Title: Adapting a normalized gradient subspace algorithm to real-valued data model [articol]
Authors: Slavnicu, Ștefan
Ciochină, Silviu
Subjects: Subspace tracking
Frequency estimation
Real-valued data
Issue Date: 2006
Publisher: Timişoara: Editura Politehnica
Citation: Slavnicu, Ștefan. Adapting a normalized gradient subspace algorithm to real-valued data model. Timişoara: Editura Politehnica, 2006
Series/Report no.: Buletinul ştiinţific al Universităţii „Politehnica” din Timişoara, România. Seria electronică şi telecomunicaţii, Tom 51(65), fasc. 2 (2006), p. 23-27
Abstract: A new gradient approach to adaptive subspace-based frequency estimation of multiple real valued sine waves is considered in this paper. The new approach proposed here combines the normalized gradient subspace tracking technique based on Oja learning rule - NOOja (for the signal subspace update) with the ESPRIT-like frequency estimation of real-valued sinusoids (for frequency values retrieval). Consequently, a new adaptive subspace-tracking algorithm for frequency estimation is proposed. The method proposed brings a significant reduction in arithmetical complexity at the same level of accuracy. The algorithm is tested in numerical simulations and compared to complex-valued NOja method.
URI: http://localhost:8080/xmlui/handle/123456789/1806
Appears in Collections:Articole stiintifice/Scientific articles

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