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The integration of data and knowledge from several sources is known as data fusion.
Y. Bar-Shalom, “On the track-to-track correlation problem,” IEEE Transactions on Automatic control , vol. 26, no. 2, pp. 571–572, 1981
1981
Earlier work this paper cites.
H. R. Hashemipour, S. Roy, and A. J. Laub, “Decentralized structures for parallel Kalman filtering,” IEEE Transactions on automatic control , vol. 33, no. 1, pp. 88–94, 1988
1988
Earlier work this paper cites.
F. White, “Data fusion lexicon,” 1991
1991
Earlier work this paper cites.
D. L. Hall and J. Llinas, “An introduction to multisensor data fusion,” Proceedings of the IEEE , vol. 85, no. 1, pp. 6–23, 1997
1997
Earlier work this paper cites.
V. Tresp, “A Bayesian committee machine,” Neural computation , vol. 12, no. 11, pp. 2719–2741, 2000
2000
Earlier work this paper cites.
K. Ito and K. Xiong, “Gaussian Filters for Nonlinear Filtering Problems,” IEEE Trans. Autom. Control , vol. 45, no. 5, May 2000
2000
Earlier work this paper cites.
P. Bromiley, “Products and convolutions of Gaussian probability density functions,” Tina-Vision Memo , vol. 3, no. 4, p. 1, 2003
2003
Earlier work this paper cites.
D. L. Hall and S. A. McMullen, Mathematical techniques in multisensor data fusion . Artech House, 2004
2004
Earlier work this paper cites.
M. M. Kokar, J. A. Tomasik, and J. Weyman, “Formalizing classes of information fusion systems,” Information Fusion , vol. 5, no. 3, pp. 189–202, 2004
2004
Earlier work this paper cites.
S. P. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Earlier work this paper cites.
C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning . Springer, 2006, vol. 4, no. 4
2006
Earlier work this paper cites.
K. Kim and G. Shevlyakov, “Why Gaussianity?” Signal Processing Magazine, IEEE , vol. 25, no. 2, pp. 102–113, March 2008
2008
Earlier work this paper cites.
K.-C. Chang, C.-Y. Chong, and S. Mori, “Analytical and computational evaluation of scalable distributed fusion algorithms,” IEEE transactions on Aerospace and Electronic Systems , vol. 46, no. 4, pp. 2022–2034, 2010
2010
Earlier work this paper cites.
T. Bailey, S. Julier, and G. Agamennoni, “On conservative fusion of information with unknown non-gaussian dependence,” in 2012 15th International Conference on Information Fusion . IEEE, 2012, pp. 1876–1883
2012
Earlier work this paper cites.
B. J. Kleijn and A. W. van der Vaart, “The Bernstein-von-Mises theorem under misspecification,” Electronic Journal of Statistics , vol. 6, pp. 354–381, 2012
2012
Earlier work this paper cites.
F. Castanedo, “A review of data fusion techniques,” The scientific world journal , vol. 2013, 2013
2013
Earlier work this paper cites.
M. S. Mahmoud and H. M. Khalid, “Distributed Kalman filtering: a bibliographic review,” IET Control Theory & Applications , vol. 7, no. 4, pp. 483–501, 2013
2013
Earlier work this paper cites.
B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of-the-art,” Information fusion , vol. 14, no. 1, pp. 28–44, 2013
2013
Earlier work this paper cites.
F. Nielsen and R. Nock, “On the chi square and higher-order chi distances for approximating f-divergences,” IEEE Signal Processing Letters , vol. 21, no. 1, pp. 10–13, 2013
2013
Cited alongside, same era.
D. Dardari, P. Closas, and P. M. Djurić, “Indoor tracking: Theory, methods, and technologies,” IEEE Transactions on Vehicular Technology , vol. 64, no. 4, pp. 1263–1278, 2015
2015
Cited alongside, same era.
M. Deisenroth and J. W. Ng, “Distributed Gaussian processes,” in International Conference on Machine Learning . PMLR, 2015, pp. 1481–1490
2015
Cited alongside, same era.
——, “Bias correction for distributed Bayesian estimators,” in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . IEEE, 2015, pp. 253–256
2015
Cited alongside, same era.
F. Lindgren and H. Rue, “Bayesian spatial modelling with R-INLA,” Journal of statistical software , vol. 63, pp. 1–25, 2015
2019
Later among the works it cites.
V. Elvira, L. Martino, D. Luengo, and M. F. Bugallo, “Generalized multiple importance sampling,” Statistical Science , vol. 34, no. 1, pp. 129–155, 2019
2019
Later among the works it cites.
J. Duník, S. K. Biswas, A. G. Dempster, T. Pany, and P. Closas, “State estimation methods in navigation: overview and application,” IEEE Aerospace and Electronic Systems Magazine , vol. 35, no. 12, pp. 16–31, 2020
2020
Later among the works it cites.
T. Meng, X. Jing, Z. Yan, and W. Pedrycz, “A survey on machine learning for data fusion,” Information Fusion , vol. 57, pp. 115–129, 2020
2020
Later among the works it cites.
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2015
Cited alongside, same era.
M. E. Campbell and N. R. Ahmed, “Distributed data fusion: Neighbors, rumors, and the art of collective knowledge,” IEEE Control Systems Magazine , vol. 36, no. 4, pp. 83–109, 2016
2016
Cited alongside, same era.
H. D. Nguyen, L. R. Lloyd-Jones, and G. J. McLachlan, “A universal approximation theorem for mixture-of-experts models,” Neural computation , vol. 28, no. 12, pp. 2585–2593, 2016
2016
Cited alongside, same era.
Z. Xing, Y. Xia, L. Yan, K. Lu, and Q. Gong, “Multisensor distributed weighted Kalman filter fusion with network delays, stochastic uncertainties, autocorrelated, and cross-correlated noises,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 48, no. 5, pp. 716–726, 2016
2016
Cited alongside, same era.
M. A. Bakr and S. Lee, “Distributed multisensor data fusion under unknown correlation and data inconsistency,” Sensors , vol. 17, no. 11, p. 2472, 2017
2017
Cited alongside, same era.
S. Julier and J. K. Uhlmann, “General decentralized data fusion with covariance intersection,” in Handbook of multisensor data fusion . CRC Press, 2017, pp. 339–364
2017
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Cited alongside, same era.
S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso, and A. M. Stuart, “Importance sampling: Intrinsic dimension and computational cost,” Statistical Science , pp. 405–431, 2017
2017
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Liu, Y.-S. Ong, X. Shen, and J. Cai, “When Gaussian process meets big data: A review of scalable GPs,” IEEE transactions on neural networks and learning systems , vol. 31, no. 11, pp. 4405–4423, 2020
2020
Later among the works it cites.
H. B. McMahan et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Wu, T. Imbiriba, J. Park, S. Kim, and P. Closas, “Personalized Federated Learning over non-IID Data for Indoor Localization,” in 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . IEEE, 2021, pp. 421–425
2021
Later among the works it cites.
2021
Later among the works it cites.
I. Achituve, A. Shamsian, A. Navon, G. Chechik, and E. Fetaya, “Personalized federated learning with Gaussian processes,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
V. Elvira and L. Martino, “Advances in importance sampling,” Wiley StatsRef: Statistics Reference Online , pp. 1–22, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Park, J. Moon, T. Kim, P. Wu, T. Imbiriba, P. Closas, and S. Kim, “Federated learning for indoor localization via model reliability with dropout,” IEEE Communications Letters , pp. 1–1, 2022
2022
Closest in time.
G. Koliander, Y. El-Laham, P. M. Djurić, and F. Hlawatsch, “Fusion of Probability Density Functions,” Proceedings of the IEEE , vol. 110, no. 4, pp. 404–453, 2022
2022
Closest in time.
A. A. Saucan, V. Elvira, P. K. Varshney, and M. Z. Win, “Information fusion via importance sampling,” IEEE Transactions on Signal and Information Processing over Networks , 2023
2023
Closest in time.