Adaptive Gauss Hermite filter for parameter and state estimation of nonlinear systems

This paper presents an adaptive Gauss Hermite filter for nonlinear signal models in the situation when the measurement noise statistics is unknown. The proposed nonlinear filter, based on the Gauss Hermite quadrature rule, can ensure satisfactory estimation performance despite the problem of unknown measurement noise statistics by online adaptation. Results of Monte Carlo Simulation demonstrate the efficacy of the proposed filter for joint estimation of parameters and states using an object tracking problem.