A framework for particle filtering in positioning, navigation and tracking problems

Author

Gustafsson, F. ; Gunnarsson, F. ; Bergman, N. ; Forssell, U. ; Jansson, J. ; Nordlund, P.-J. ; Karlsson, R.

Author_Institution

Dept. of Electr. Eng., Linkoping Univ., Sweden

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

34

Lastpage

37

Abstract

A framework for positioning, navigation and tracking problems using particle filters (recursive Monte Carlo methods) is developed. Automotive and airborne applications, approached in this framework, have proven a numerical advantage over classical Kalman filter based algorithms. Here the use of non-linear measurement models and non-Gaussian measurement noise is the main explanation for the improvement in accuracy, and models for relevant sensors are surveyed

Keywords

Monte Carlo methods; aircraft navigation; filtering theory; inertial navigation; measurement errors; tracking filters; airborne applications; aircraft positioning; automotive applications; integrated navigation; navigation problems; nonGaussian measurement noise; nonlinear measurement models; particle filtering; positioning problems; recursive Monte Carlo methods; sensors; statistical signal processing; tracking problems; Equations; Filtering; Navigation; Noise measurement; Particle filters; Particle tracking; Position measurement; Sensor phenomena and characterization; Signal processing algorithms; Ultrasonic variables measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955215

Filename

955215