Preprint Article Version 1 This version is not peer-reviewed

A Hybrid Adaptive Unscented Kalman Filter Algorithm

Version 1 : Received: 15 March 2017 / Approved: 17 March 2017 / Online: 17 March 2017 (01:49:42 CET)

How to cite: He, J.; Zhang, Q.; Hu, Q.; Sun, G. A Hybrid Adaptive Unscented Kalman Filter Algorithm. Preprints 2017, 2017030127 (doi: 10.20944/preprints201703.0127.v1). He, J.; Zhang, Q.; Hu, Q.; Sun, G. A Hybrid Adaptive Unscented Kalman Filter Algorithm. Preprints 2017, 2017030127 (doi: 10.20944/preprints201703.0127.v1).

Abstract

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

Subject Areas

hybrid adaptive; unscented kalman filtering; maximum a posteriori; maximum likelihood criterion

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