An approximate nonlinear filter for simultaneous estimation of states and parameters in linear stochastic systems

An approximate nonlinear filter for simultaneous estimation of states and parameters in linear stochastic systems is derived based on some asymptotic considerations. The convergence properties of the approximate nonlinear filter as a parameter estimator for linear stochastic systems are examined and comparisons with other recursive identification algorithms are made.