An Adaptive inverse-QR Recursive Levenberg-Marquardt Algorithm

In this paper, we present the adaptive inversesquare root recursive Levenberg-Marquardt (inverse-QR RLM) algorithm. We introduce how to formulate the proposed inverseQR RLM algorithm based on the least squares criterion for nonlinear adaptive filtering. We describe briefly about the inverseQR RLM algorithm which can preserve the Hermitian symmetry of inverse autocorrelation matrix with the method of modified product of QR-decomposition and its Hermitian transpose. This symmetrical property leads to the benefit of parallel implementation. Simulation results show that the proposed algorithm can obtain a fast convergence rate to unknown channel estimation or system identification.