@inproceedings{Luengo2015b,
title = {Efficient Linear Combination of Partial Monte Carlo Estimators},
author = {Luengo, David and Martino, Luca and Elvira, Victor and Bugallo, Monica F.},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178742 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_luengo.pdf},
doi = {10.1109/ICASSP.2015.7178742},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4100--4104},
publisher = {IEEE},
address = {Brisbane},
abstract = {In many practical scenarios, including those dealing with large data sets, calculating global estimators of unknown variables of interest becomes unfeasible. A common solution is obtaining partial estimators and combining them to approximate the global one. In this paper, we focus on minimum mean squared error (MMSE) estimators, introducing two efficient linear schemes for the fusion of partial estimators. The proposed approaches are valid for any type of partial estimators, although in the simulated scenarios we concentrate on the combination of Monte Carlo estimators due to the nature of the problem addressed. Numerical results show the good performance of the novel fusion methods with only a fraction of the cost of the asymptotically optimal solution.},
keywords = {covariance matrices, efficient linear combination, Estimation, fusion, Global estimator, global estimators, least mean squares methods, linear combination, minimum mean squared error estimators, Monte Carlo estimation, Monte Carlo methods, partial estimator, partial Monte Carlo estimators, Xenon},
pubstate = {published},
tppubtype = {inproceedings}
}

In many practical scenarios, including those dealing with large data sets, calculating global estimators of unknown variables of interest becomes unfeasible. A common solution is obtaining partial estimators and combining them to approximate the global one. In this paper, we focus on minimum mean squared error (MMSE) estimators, introducing two efficient linear schemes for the fusion of partial estimators. The proposed approaches are valid for any type of partial estimators, although in the simulated scenarios we concentrate on the combination of Monte Carlo estimators due to the nature of the problem addressed. Numerical results show the good performance of the novel fusion methods with only a fraction of the cost of the asymptotically optimal solution.

@inproceedings{Bugallo2009,
title = {Cost-Reference Particle Filters and Fusion of Information},
author = {Bugallo, Monica F. and Maiz, Cristina S. and Miguez, Joaquin and Djuric, Petar M.},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785936},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {286--291},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.},
keywords = {costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement},
pubstate = {published},
tppubtype = {inproceedings}
}

Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.