Recursive local orthogonality filtering

Recursive local orthogonality (RLO) is a stochastic Newton algorithm to achieve a condition we term local orthogonality, which only requires unimodal symmetric densities with continuous nonzero second derivatives near the origin. For Gaussian systems, RLO reduces to recursive least squares. Local orthogonality is both a subset of median orthogonality and a form of constrained maximum-likelihood optimization. Fast multichannel time and multichannel frequency domain implementations are given. Simulations show the utility for system identification and inverse modeling.