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

• Published in: Conference record - Asilomar Conference on Signals, Systems, & Computers pp. 205 - 212
• Main Author: Agee, Brian G.
• Format: Conference Proceeding
• Language: English
• 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
• ISSN: 1058-6393
• DOI: 10.1109/ACSSC.2013.6810260

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.