ガウシアンフィルタの新たな視点と拡張
A New Perspective and Extension of the Gaussian Filter
ガウシアンフィルタを変分推論の観点から捉え直し、信念の表現を一般化したFeature Gaussian Filterを提案。非線形観測モデルを持つ系で標準手法より大幅な性能向上を示した。
著者: Manuel Wüthrich, Sebastian Trimpe, Daniel Kappler, Stefan Schaal
分類: cs.RO
原文アブストラクト
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF from a variational-inference perspective. We analyse how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.
