Fast converging robust adaptive arrays
Open Access
- 13 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 216-221
- https://doi.org/10.1109/nrc.2001.922980
Abstract
A pseudo-median canceller is introduced as the canonical processor of a robust adaptive array method which significantly reduces the deleterious effects of non-Gaussian, real-world noise and interference (outliers) on typical array performance metrics such as (normalized) output noise power residue and signal-to-interference-plus-noise ratio (SINR). In addition, the proposed structure offers natural protection against signal cancellation, or equivalently, against an increase in the output noise power residue, when weight-training data contains desired signal components. The convergence rate is shown to be commensurate with sample matrix inversion (SMR) methods for Gaussian noise and interference, and convergence is essentially unaffected when outliers are added to the Gaussian weight-training data, while non-robust SMI methods slow significantly under the same circumstances.Keywords
This publication has 4 references indexed in Scilit:
- Fast converging adaptive processor or a structured covariance matrixIEEE Transactions on Aerospace and Electronic Systems, 2000
- Convergence properties of Gram-Schmidt and SMI adaptive algorithms. IIIEEE Transactions on Aerospace and Electronic Systems, 1991
- Convergence properties of Gram-Schmidt and SMI adaptive algorithmsIEEE Transactions on Aerospace and Electronic Systems, 1990
- Rapid Convergence Rate in Adaptive ArraysIEEE Transactions on Aerospace and Electronic Systems, 1974