2011
Balasingam, Balakumar; Bolic, Miodrag; Djuric, Petar M; Miguez, Joaquin
Efficient Distributed Resampling for Particle Filters Proceedings Article
En: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3772–3775, IEEE, Prague, 2011, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Copper, Covariance matrix, distributed resampling, Markov processes, Probability density function, Sequential Monte-Carlo methods, Signal processing, Signal processing algorithms
@inproceedings{Balasingam2011,
title = {Efficient Distributed Resampling for Particle Filters},
author = {Balakumar Balasingam and Miodrag Bolic and Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5947172},
issn = {1520-6149},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {3772--3775},
publisher = {IEEE},
address = {Prague},
abstract = {In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm.},
keywords = {Approximation algorithms, Copper, Covariance matrix, distributed resampling, Markov processes, Probability density function, Sequential Monte-Carlo methods, Signal processing, Signal processing algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm.