A computationally efficient method for online joint state inference anddynamical model learning is presented . The dynamical model combines an a prioriknown state-space model with a radial basis function expansion representingunknown system dynamics . The method uses an extended Kalman filterapproach to jointly estimate the state of the system and learn the systemproperties . The model is inherently adaptive and can learnunknown and changing system dynamics on-the-fly . By usingcompact radial basis functions and an approximate Kalman gain, thecomputational complexity is considerably reduced compared to similarapproaches . The approximation works well when the system dynamics exhibitlimited correlation between points well separated in the state- space domain . It is exemplified via two intelligent vehicle applications where it isshown to have essentially identical system dynamics estimation performance and (ii) be real-

Author(s) : Anton Kullberg, Isaac Skog, Gustaf Hendeby

Links : PDF - Abstract

Code :

https://github.com/nhynes/abc


Coursera

Keywords : state - system - dynamics - model - space -

Leave a Reply

Your email address will not be published. Required fields are marked *