Bayes filter for dynamic coordinate measurements - Accuracy improvment, data fusion and measurement uncertainty evaluation

This paper presents a novel methodology to improve the measurement accuracy of dynamic measurements. This is achieved by deducing an online Bayes optimal estimate of the true measurand given uncertain, noisy or incomplete measurements within the framework of sequential Monte Carlo methods. The estimation problem is formulated as a general Bayesian inference problem for nonlinear dynamic systems. The optimal estimate is represented by probability density functions, which enable an online, probabilistic data fusion as well as a Bayesian measurement uncertainty evaluation corresponding to the “Guide to the expression of uncertainty in measurement“. The efficiency and performance of the proposed methodology is verified and shown by dynamic coordinate measurements.