The article presents studies on the estimation quality of a particle filter applied to small multidimensional objects. For the purposes of the article, a new type of network has been proposed, in which each node is associated with one state variable. Based on performed simulations it has been found that particle filter implemented for small systems (1- or 2-dimensional) is a good choice. However, for larger objects Kalman filter may return better results (it depends on the chosen particles number). This is due to the exponential dependence of needed particles number to the object dimension. It has been also observed that the particle filter, in comparison to the Extended Kalman filter, better estimates the state variables which are well metered, and simultaneously worse estimates the state variables which are worse metered. Possible approaches for objects with a greater number of state variables also have been adduced, including the dispersed particle filter.
['Piotr Kozierski', 'Talar Sadalla', 'Adam Owczarkowski', 'Szymon Drgas']
Particle filter in multidimensional systems