Name
Abstract | ||
|---|---|---|
Most adaptive filtering algorithms couple performance with complexity. Over the last 15 years, a class of algorithms, termed "affine projection" algorithms, have given system designers the capability to tradeoff performance with complexity. By changing parameters and the size/scale of data used to update the coefficients of an adaptive filter but without fundamentally changing the algorithm structure, a system designer can radically change the performance of the adaptive algorithm. This paper discusses low-complexity data reusing algorithms that are closely related to affine projection algorithms. This paper presents various low-complexity and highly flexible schemes for improving convergence rates of adaptive algorithms that utilize data reusing strategies. All of these schemes are unified by a row projection framework in existence for more than 65 years. This framework leads to the classification of all data reusing and affine projection methods for adaptive filtering into two categories: the Kaczmarz and Cimmino methods. Simulation and convergence analysis results are presented for these methods under a number of conditions. They are compared in terms of convergence rate performance and computational complexity. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TSP.2003.821338 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
computational complexity,adaptive filter,adaptive filters,convergence rate,indexing terms,system design | Convergence (routing),Signal processing,Mathematical optimization,Reuse,Projection method,Rate of convergence,Adaptive filter,Adaptive algorithm,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
52 | 2 | 1053-587X |
Citations | PageRank | References |
12 | 0.92 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert A. Soni | 1 | 12 | 0.92 |
| Kyle Gallivan | 2 | 889 | 154.22 |
| W. Kenneth Jenkins | 3 | 73 | 15.29 |