A generalized framework for development of partially-updated signal and parameter estimation algorithms based on subspace optimization constraints

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 fun...

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Bibliographic Details
Published inConference record - Asilomar Conference on Signals, Systems, & Computers pp. 205 - 212
Main Author Agee, Brian G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Adaptive signal processing
Algorithm design and analysis
Complexity theory
Interference
Optimization
partial update algorithms
Program processors
Signal processing algorithms
Signal to noise ratio
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Summary: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.
ISSN:1058-6393
DOI:10.1109/ACSSC.2013.6810260