Abstract
In this paper authors present a new approaches to the hybrid Kalman filtering and modified hybrid Kalman filtering, with the changed order of methods inside (Unscented Kalman Filter and Extended Kalman Filter). For these algorithms, the modification based on double use of Hybrid Kalman Filters (Excented and Unscented) has been proposed. This new modification has been checked for Hybrid Kalman Particle Filters too, for the variable number of particles. Based on the obtained results, one can see that duplication of hybrid filters can improve the estimation quality.
Highlights
Very important branch of science, especially in the noisy environment of measurements, is state estimation
This paper develops the problem of state estimation of dynamical systems and refers to research from [1], where authors proposed modification of Hybrid Kalman Filter and Hybrid Kalman Particle Filter [2]
In Hybrid Kalman Particle Filter (HKPF), the Probability Density Function (PDF) is used to draw the particles and weights are determined from the results of Hybrid Kalman Filter (HKF), which combine Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) algorithms
Summary
Very important branch of science, especially in the noisy environment of measurements, is state estimation. This paper develops the problem of state estimation of dynamical systems and refers to research from [1], where authors proposed modification of Hybrid Kalman Filter and Hybrid Kalman Particle Filter [2]. There are a lot of applications of state estimation [3, 4] and many different types of estimation methods [57]. The need and use of state estimation have been described in details in Introduction of [1]. Conclusions are presented in the last section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
