Authors:
Guoliang Liu
;
Florentin Wörgötter
and
Irene Markelić
Affiliation:
University of Göttingen, Germany
Keyword(s):
Nonlinear Estimation, Sensor Fusion, Unscented Information Filter, Square-root Unscented Information Filter.
Related
Ontology
Subjects/Areas/Topics:
Data Manipulation
;
Distributed and Collaborative Signal Processing
;
Sensor Data Fusion
;
Sensor Networks
;
Signal Processing
;
Statistical and Adaptive Signal Processing
Abstract:
This paper presents a new recursive Bayesian estimation method, which is the square-root unscented information filter (SRUIF). The unscented information filter (UIF) has been introduced recently for nonlinear system estimation and sensor fusion. In the UIF framework, a number of sigma points are sampled from the probability distribution of the prior state by the unscented transform and then propagated through the nonlinear dynamic function and measurement function. The new state is estimated from the propagated sigma points. In this way, the UIF can achieve higher estimation accuracies and faster convergence rates than the extended information
filter (EIF). As the extension of the original UIF, we propose to use the square-root of the covariance in the SRUIF instead of the full covariance in the UIF for estimation. The new SRUIF has better numerical properties than the original UIF, e.g., improved numerical accuracy, double order precision and preservation of symmetry.