Abstract
The least squares based affine projection algorithm (APA) is sensitive to outliers/impulsive noise in the desired data. A novel robust APA over distributed networks scenario is proposed in this manuscript, which is based on the rank based robust estimator named Wilcoxon norm. The proposed diffusion Wilcoxon affine projection algorithm (DWx-APA) based on pseudo least squares formulation is robust against outliers in the desired data and converges faster than the diffusion minimum Wilcoxon norm algorithm (DWx). The QR based diffusion minimum Wilcoxon norm (QR-DWx) algorithm is also robust against outliers in the desired data and converges faster than the DWx and the proposed DWx-APA, but the computational complexity is very high in comparison to its counterparts. The proposed DWx-APA is a compromise between DWx and QR-DWx in terms of computational complexity and convergence speed. The mean stability, tracking capability and computational complexity of the proposed algorithm are investigated. The simulation based experiments and analysis validate that the proposed algorithm performs better than the state-of-the-art algorithms in the presence of color data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.