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}
}
Achutegui, Katrin; Miguez, Joaquin
A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks Proceedings Article
En: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 81–84, IEEE, San Juan, 2011, ISBN: 978-1-4577-2105-2.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN
@inproceedings{Achutegui2011,
title = {A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks},
author = {Katrin Achutegui and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6136051},
isbn = {978-1-4577-2105-2},
year = {2011},
date = {2011-01-01},
booktitle = {2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {81--84},
publisher = {IEEE},
address = {San Juan},
abstract = {We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.},
keywords = {Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN},
pubstate = {published},
tppubtype = {inproceedings}
}