Joint Estimation of Parameters and States of Nonlinear Systems using Adaptive Divided Difference Filter
نویسندگان
چکیده
An adaptive Divided Difference filter for joint estimation of parameters and states of a nonlinear system has been proposed in this work. The adaptive filter is proposed for improved estimation specifically in the situation when knowledge about the process noise statistics is unavailable. The innovation sequence has been employed for adaptation of the unknown process noise covariance. The evolved Adaptive Divided Difference filter has been evaluated with a benchmark nonlinear problem of ballistic object tracking. With the help of simulation, it has been demonstrated that even though the process noise covariance is unknown, the performance of proposed filter is superior compared to a non adaptive Divided Difference filter.
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