Abstract
The structure of the input covariance matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the covariance matrix inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the covariance matrix that results from this formulation.
Original language | English (US) |
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Pages (from-to) | 267-270 |
Number of pages | 4 |
Journal | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
Volume | 1 |
State | Published - 1997 |
Event | Proceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2) - Pacific Grove, CA, USA Duration: Nov 3 1996 → Nov 6 1996 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Networks and Communications