An adaptive recursive algorithm based on non-quadratic function of the error

• Published in: Signal processing Vol. 92; no. 4; pp. 853 - 856
• Main Authors: da Silva, Cristiane, Santana, Ewaldo, Aguiar, Enio, de Araújo, Marcos A.F., Barros, Allan Kardec
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2012; Elsevier
• Subjects: Adaptive filters; Algorithms; Applied sciences; Computational efficiency; Convergence; Detection, estimation, filtering, equalization, prediction; Errors; Exact sciences and technology; Information, signal and communications theory; Mathematical analysis; Mathematical models; Non-quadratic function and convergence time; Recursive; Recursive filter adaptive filter; Signal and communications theory; Signal, noise; Telecommunications and information theory; Recursive filter adaptive filter; Non-quadratic function and convergence time; Performance evaluation; Infinite impulse response filter; Second order; Adaptive algorithm; Adaptive filtering; Order statistic; Adaptive filter; Recursive algorithm; Computational complexity; Convergence speed; Statistical method; Least squares method; Gaussian signal; Convergence rate; Signal processing; Quadratic function
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2011.09.019

In adaptive filtering, several algorithms were developed to get faster convergence and lower misadjustment, but rely on second order statistics which are optimum only for Gaussian signals. In this work we propose a recursive filter by modifying the performance surface to a non-quadratic function applied upon the error. As a result, the equations are simple, elegant, and yielded faster convergence and lower misadjustment when compared to the RLS, keeping equivalent computational cost.