Adaptive divided difference filter for nonlinear systems with non-additive noise

An Adaptive Divided Difference filter has been proposed for the systems with non additive Gaussian noise in the situations when noise statistics is unknown. In face of unknown noise statistics the proposed filter can adapt the unknown measurement noise covariance (Ü) incorporating the steps for adaptation in the non adaptive algorithm of Divided Difference filter. Satisfactory estimation performance of the proposed filter is ensured by online adaptation of the unknown measurement noise covariance with guaranteed positive definiteness of adapted R. Simulation results obtained from the case study demonstrate that the adapted measurement noise covariance converges to its truth value and can also successfully track the truth value when it is time varying. From the Monte Carlo study it is observed that the performance of the proposed filter is superior compared to its non adaptive version when the noises are non additive and the measurement noise statistics remains unknown.