Variational Bayesian Adaptive Kalman Filtering Algorithm Based on Parameter Decoupling Method
In the context of state estimation problems under mismatched noise covariance matrices,a parameter-decoupled variational Bayesian adaptive Kalman filter(PD-VB-AKF)algorithm is proposed in the paper within the framework of variational Bayesian(VB)method.The filter can be applicable when both the process noise covariance matrix(PNCM)and the measurement noise covariance matrix(MNCM)are unknown.The proposed algorithm chooses the predicted error covariance matrix(PECM)as the variable to optimize through variational techniques and introduces a Markov evolution model to construct the parameter-decoupled variational inference model.Furthermore,it utilizes the fixed-point iteration optimization to solve the joint posterior probability distribution of the state,PECM and MNCM,and outlines the convergence criteria of the algorithm.The simulation results validate the effectiveness of proposed algorithm.
adaptive state estimationKalman filteringvariational Bayesiannoise covariance matricesparame-ter decoupling
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