The least-mean-square (LMS) adaptive algorithm is probably the best known and most widely used real-time adaptive filtering algorithm due to its simple computational requirements. However, as VLSI digital processors become cheaper and more readily available, the question arises as to whether more effective real-time algorithms can be found that take advantage of increased computational resources as they become available. It has been shown in the literature that a real time decomposition of the incoming signal into a set of orthogonal components, and a subsequent adaptation on these individual components, leads to improved performance. The authors discuss the role of orthogonal transformation in adaptive noise canceling and demonstrate the use of the Walsh-Hadamard transform (WHT) for improving performance. The results show that the WHT is capable of decomposing the input signal into orthogonal channels so that the transform domain can be whitened, although the effects of leakage prevent the orthogonalization from being complete.
|Original language||English (US)|
|Number of pages||4|
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|State||Published - Jan 1 1986|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering