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• W2034985390 abstract "A generalized framework for development of subspace constrained partially-updated (SCPU) signal and parameter estimation algorithms is proposed and demonstrated via analysis and computer simulation. Conventional partial-update (PU) methods are first reviewed and interpreted as a sequence of cost function optimizations subject to a hard parameter constraint. The SCPU method is then introduced as an equivalent optimization subject to a soft subspace constraint. It is shown that the new method removes adaptive misadjustment inherent to conventional PU methods, and allows generalization of the partial-update methods to much broader classes of signal and parameter estimation algorithms, including blind and nonblind ML estimation methods." @default.
• W2034985390 created "2016-06-24" @default.
• W2034985390 creator A5058078940 @default.
• W2034985390 date "2013-11-01" @default.
• W2034985390 modified "2023-09-24" @default.
• W2034985390 title "A generalized framework for development of partially-updated signal and parameter estimation algorithms based on subspace optimization constraints" @default.
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• W2034985390 doi "https://doi.org/10.1109/acssc.2013.6810260" @default.
• W2034985390 hasPublicationYear "2013" @default.
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