Adaptive Gauss Hermite filter for parameter varying nonlinear systems

This paper presents an adaptive sigma point filter based on Gauss Hermite quadrature rule for estimation of unknown time varying parameters and states of nonlinear systems. An adaptive filter is required for such problems because of the unknown parameter variation which often makes the knowledge of the process noise covariance unavailable. The performance of the proposed filter which adapts to the time varying process noise is evaluated using a case study. The simulation results demonstrate that the proposed filter apart from estimating the states can successfully track and estimate the time varying parameter. From Monte Carlo study it is further observed that the performance of the adaptive Gauss Hermite filter is superior compared with its non adaptive version in the perspective of time varying parameter estimation.