Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation

This paper examines the effect of the moment-matched single Gaussian approximation, which is made in various multiple-model filtering applications to approximate a Gaussian mixture, on the Bayesian filter performance. The estimation error caused by the approximation is analysed for both the prediction and the measurement updates of a Bayesian filter. An approximate formula is found for the covariance of the error caused by the approximation for a general Gaussian mixture with arbitrary components. The calculated error covariance is used for obtaining a mixed multiple-model estimation algorithm which has a performance near that of GPB2 with less computations.


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In this study, we propose process noise covariance matrix adaptation (Q-adaptation) for the Singular Value Decomposition (SVD) aided Unscented Kalman Filter (UKF) algorithm. The main aim is to make the algorithm adaptive against the changes in the process noise covariance. The SVD-aided Adaptive UKF (SaAUKF) estimates the attitude and attitude rate of a nano satellite. We implement the SVD method in the algorithm's first phase using magnetometer and sun sensor measurements. It estimates the attitude of the ...
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Citation Formats
U. Orguner, “Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation,” INTERNATIONAL JOURNAL OF CONTROL, pp. 952–967, 2007, Accessed: 00, 2020. [Online]. Available: