TY - GEN
T1 - Online adaptation of the number of particles of SMC methods
AU - Elvira, Victor
AU - Míguez, Joaquín
AU - Djurić, Petar M.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - Particle filtering is a widely used sequential methodology that approximates probability distributions by using discrete random measures composed of weighted particles. A large number of particles improves the quality of the approximation but increases the computational requirements. Although there exists an abundant variety of particle filtering algorithms in the literature, there is lack of work devoted to selecting or adapting the number of particles systematically. In this paper we propose a novel methodology for online assessment of convergence of particle filtering. Based on theoretical analysis of the assessment, we propose an algorithm for the adaptation of the number of particles in online manner. The performance of the proposed algorithm is demonstrated for two state-space models.
AB - Particle filtering is a widely used sequential methodology that approximates probability distributions by using discrete random measures composed of weighted particles. A large number of particles improves the quality of the approximation but increases the computational requirements. Although there exists an abundant variety of particle filtering algorithms in the literature, there is lack of work devoted to selecting or adapting the number of particles systematically. In this paper we propose a novel methodology for online assessment of convergence of particle filtering. Based on theoretical analysis of the assessment, we propose an algorithm for the adaptation of the number of particles in online manner. The performance of the proposed algorithm is demonstrated for two state-space models.
KW - adaptive complexity
KW - computational complexity
KW - convergence analysis
KW - convergence assessment
KW - Particle filtering
KW - predictive distribution
KW - sequential Monte Carlo
UR - https://www.scopus.com/pages/publications/84973343105
U2 - 10.1109/ICASSP.2016.7472504
DO - 10.1109/ICASSP.2016.7472504
M3 - Conference contribution
AN - SCOPUS:84973343105
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4378
EP - 4382
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -