An Adaptive Estimation Method for Noise Parameter Matrices of Unscented Kalman Filter Based on Deep Q-Network
For a class of nonlinear systems with uncertainty in noise parameter,how to adaptively adjust the noise parameters to achieve high-precision anti-disturbance information fusion is an urgent problem to be solved.In information fusion,the accurate estimation of system noise parameters is the primary consideration in filter design.When the system noise parameters are inconsistent with the prior information,the Kalman filters cannot achieve accurate state estimation,leading to a decline in the performance of information fusion.To address the information fusion problem of a class of nonlin-ear systems with unknown noise parameters,a deep Q-network is constructed and trained with the help of deep reinforce-ment learning methods to adaptively adjust the size of the system noise matrix,which is then used to design extended Kal-man filters and unscented Kalman filters to realize the interference-resistant information fusion.To verify the effectiveness of the method,numerical simulation experiments are conducted.The results show that with the adaptive estimation of the noise parameters based on deep Q-network,the mean square error of the unscented Kalman filter is kept within 0.02 and the mean square error of the extended Kalman filter is kept around 0.6,which effectively improves the accuracy of the tradi-tional Kalman filter and realize the anti-disturbance information fusion under unknown noise parameters.
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