This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximations for global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. All filters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-only tracking. The results show that the stochastic integration filter provides better accuracy than the Monte-Carlo Kalman filter and the ensemble Kalman filter with lower computational costs.
Random-point-based filters: analysis and comparison in target tracking
IEEE Transactions on Aerospace and Electronic Systems ; 51 , 2 ; 1403-1421
01.04.2015
1451635 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimization of point target tracking filters
IEEE | 2000
|PAPERS - Optimization of Point Target Tracking Filters
Online Contents | 2000
|Comparison of several space target tracking filters
SPIE | 2009
|Tema Archiv | 1974
|Tracking a Ballistic Target: Comparison of Several Nonlinear Filters
Online Contents | 2002
|