Stochastic Fusion Techniques for State Estimation

Abstract

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since their information is most likely to be trustworthy. This study stochastically investigates the three most common fusion techniques: Kalman filtering, particle filtering and Bayesian probability (which is the basis of other techniques). The paper focuses on using fusion techniques in the context of state estimation for dynamic systems to improve reliability and accuracy. The fusion methods are investigated using different types of datasets to find out their performance and accuracy in state estimation.