Title :
On the L4 convergence of particle filters with general importance distributions
Author :
Mbalawata, Isambi S. ; Sarkka, Simo
Author_Institution :
Lappeenranta Univ. of Technol., Lappeenranta, Finland
Abstract :
In this paper we extend the L4 proof of Hu et al. (2008) from bootstrap type of particle filters to particle filters with general importance distributions. The result essentially shows that with general importance distributions the particle filter converges provided that the importance weights are bounded. By numerical simulations we also show that this condition is often also a practical requirement for a good performance of a particle filter.
Keywords :
bootstrapping; particle filtering (numerical methods); statistical analysis; statistical distributions; bootstrap type; general importance distributions; particle filters; Approximation methods; Atmospheric measurements; Bayes methods; Convergence; Gaussian distribution; Monte Carlo methods; Particle measurements; Particle filter; convergence; importance distribution; unbounded function;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6855168
