This dissertation examines the performance of self-tuning adaptive filters in non-stationary environments and deals with extensions to conventional adaptive filters that lead to enhanced performance. A number of the available self-tuning adaptive filters, called doubly adaptive filters for the present purposes, are critically examined and three new schemes are proposed. The first and second are based on the normalized least-mean-squares (NLMS) adaptive filter, and their formulations are contrived to minimize the misadjustment in a convergent scenario and random walk scenario, respectively. The first of these filters, called reduced adaptation state estimation (RASE), achieves performance near that of the recursive-least squares (RLS) algorithm under known additive noise statistics and moderately correlated input samples. The development of the second proposed filter introduces the idea of having more than one adaptive filter applied in parallel to the same input and desired signals. This concept, called parallel adaptation (PA), is applied in both NLMS and RLS contexts in order to achieve optimal steady-state misadjustment in a random walk scenario. Numerous simulation results are presented that support the present analysis and demonstrate the effectiveness of the proposed algorithms in a number of different nonstationary environments. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/9660 |
| Date | 10 July 2018 |
| Creators | Peters, S. Douglas |
| Contributors | Antoniou, Andreas |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.003 seconds