Title :
The marginalized particle filter in practice
Author :
Schön, Thomas B. ; Karlsson, Rickard ; Gustafsson, Fredrik
Author_Institution :
Dept. of Electr. Eng., Linkoping Univ.
Abstract :
The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper will illustrate several positioning and target tracking applications, solved using the marginalized particle filter. Furthermore, we analyze several properties of practical importance, such as its computational complexity and how to cope with quantization effects
Keywords :
Gaussian noise; adaptive Kalman filters; computational complexity; particle filtering (numerical methods); position control; quantisation (signal); target tracking; Gaussian noise; Kalman filter; computational complexity; linear sub-structure; marginalized particle filter; positioning; quantization effects; target tracking; Algorithm design and analysis; Automotive engineering; Computational complexity; Gaussian noise; Particle filters; Quantization; Radar tracking; State estimation; Target tracking; Yttrium;
Conference_Titel :
Aerospace Conference, 2006 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
0-7803-9545-X
DOI :
10.1109/AERO.2006.1655922
