Maximum likelihood gradient-based iterative estimation for multivariable systems

This study concerns the parameter identification issues for a class of multivariable systems with moving average noise. The main contributions are to transform a multivariable system into several subsystems for reducing the computational complexity by means of the decomposition technique and to deal with the coloured noise for improving the estimation accuracy by using the maximum likelihood principle and the iterative estimation theory. As the maximum likelihood gradient-based iterative algorithm makes sufficient use of all the observed data at each iteration, the parameter estimation accuracy can be enhanced. The numerical simulation results demonstrate that the proposed algorithm has faster convergence rates and better tracking performance than the compared multivariable extended stochastic gradient algorithm.