Multi-innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

This article considers the parameter estimation problems of linear multivariable systems with unknown disturbances. For the parameter matrices in the multivariable systems, the model decomposition technique is used to reduce the computational complexity by decomposing the multivariable system into several subsystems with only the parameter vectors. By means of the negative gradient search, a decomposition-based maximum likelihood recursive extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, by introducing the multi-innovation identification theory, a decomposition-based maximum likelihood multi-innovation extended stochastic gradient algorithm is proposed. The simulation results illustrate the effectiveness of the proposed algorithms.