Spatial gaussian process regression with mobile sensor networks

IEEE Transactions on Neural Networks and Learning Systems

Volumn 23, Issue 8, 2012, Pages 1279-1290

  Gu, Dongbing ^a   Hu, Huosheng ^a  

^a UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Coverage control; Gaussian process regression (GPR); mobile sensor networks; spatiotemporal modeling

Indexed keywords

COMPACTLY SUPPORTED; COVERAGE CONTROL; GAUSSIAN PROCESS REGRESSION; MOBILE SENSOR NETWORKS; MOBILE WIRELESS SENSOR NETWORK; OPTIMIZATION ALGORITHMS; SPARSE GAUSSIAN PROCESS; SPATIOTEMPORAL MODELING;

ALGORITHMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); SENSOR NODES;

REGRESSION ANALYSIS;

EID: 84876924574     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2200694     Document Type: Article

References (29)

1. L. F. Merino, J. R. Caballero, J. M. de Dios, and A. O. Ferruz, "A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires," J. Field Robot., vol. 23, no. 3, pp. 165-184, 2006.
2. C. E. Corrigan, G. C. Roberts, M. V. Ramana, D. Kim, and V. Ramanathan, "Capturing vertical profiles of aerosols and black carbon over the Indian ocean using autonomous unmanned aerial vehicles," Atmosp. Chem. Phys. Discuss., vol. 7, no. 4, pp. 11429-11463, 2007.
3. N. E. Leonard, D. Paley, F. Lekien, R. Sepulchre, D. M. Fratantoni, and R. Davis, "Collective motion, sensor networks and ocean sampling," Proc. IEEE, vol. 95, no. 1, pp. 48-74, Jan. 2007.
4. M. Lazaro-Gredilla and A. R. Figueiras-Vidal, "Marginalized neural network mixtures for large-scale regression," IEEE Trans. Neural Netw., vol. 21, no. 8, pp. 1345-1351, Aug. 2010.
5. G. Skolidis and G. Sanguinetti, "Bayesian multitask classification with Gaussian process priors," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2011-2021, Dec. 2011.
6. Y. Miche, A. Sorjamaa, P. Bas, O. Simula, C. Jutten, and A. Lendasse, "OP-ELM: Optimally pruned extreme learning machine," IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 158-162, Jan. 2010.
7. C. K. I. Williams and C. E. Rasmussen, "Gaussian processes for regression," in Advances in Neural Information Processing Systems 8, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, Eds. Cambridge, MA: MIT Press, 1996, pp. 514-520.
8. D. J. C. MacKay, "Introduction to Gaussian processes," in Neural Networks and Machine Learning, vol. 168, C. M. Bishop, Ed. Berlin, Germany: Springer-Verlag, 1998, pp. 133-165.
9. C. E. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. Cambridge MA: MIT Press, 2006.
10. A. Krause, A. Singh, and C. Guestrin, "Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies," J. Mach. Learn. Res., vol. 9, pp. 235-284, Feb. 2008.
11. R. Stranders, A. Rogers, and N. Jennings, "A decentralized, on-line coordination mechanism for monitoring spatial phenomena with mobile sensors," in Proc. 2nd Int. Workshop Agent Technol. Sensor Netw., Estoril, Portugal, 2008, pp. 1-7.
12. C. Stachniss, C. Plagemann, and A. J. Lilienthal, "Learning gas distribution models using sparse Gaussian process mixtures," Auton. Robots, vol. 26, nos. 2-3, pp. 187-202, 2009.
13. J. L. Ny and G. Pappas, "On trajectory optimization for active sensing in Gaussian process models," in Proc. IEEE Conf. Decision Control, Shanghai, China, Dec. 2009, pp. 6286-6292.
14. J. Cortes, "Distributed Kriged Kalman filter for spatial estimation," IEEE Trans. Autom. Control, vol. 54, no. 12, pp. 2816-2827, Dec. 2009.
15. J. Choi, J. Lee, and S. Oh, "Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes," in Proc. Amer. Control Conf., Seattle, WA, 2008, pp. 1-6.
16. A. Singh, F. Ramos, H. D. Whyte, and W. J. Kaiser, "Modeling and decision making in spatio-temporal processes for environmental surveillance," in Proc. IEEE Int. Conf. Robot. Autom., Anchorage, AK, May 2010, pp. 5490-5497.
17. M. Schwager, D. Rus, and J. Slotine, "Decentralized, adaptive converage control for networked robots," Int. J. Robot. Res., vol. 28, no. 3, pp. 357-375, 2009.
18. K. M. Lynch, I. B. Schwartz, P. Yang, and R. A. Freeman, "Decentralized environmental modeling by mobile sensor networks," IEEE Trans. Robot., vol. 24, no. 3, pp. 710-724, Jun. 2008.
19. S. Martinez, "Distributed interpolation schemes for field estimation by mobile sensor networks," IEEE Trans. Control Syst. Technol., vol. 18, no. 2, pp. 491-500, Mar. 2010.
20. J. Quinonero-Candela and C. E. Rasmussen, "A unifying view of sparse approximate Gaussian process regression," J. Mach. Learn. Res., vol. 6, pp. 1935-1959, Dec. 2005.
21. L. Csato and M. Opper, "Sparse online Gaussian processes," Neural Comput., vol. 14, no. 2, pp. 641-669, 2002.
22. M. Seeger, C. K. I. Williams, and N. Lawrence, "Fast forward selection to speed up sparse Gaussian process regression," in Proc. 9th Int. Workshop Artif. Intell. Stat., 2003, pp. 1-8.
23. Z. Wu, "Compactly supported positive definite radial functions," Adv. Comput. Math., vol. 4, no. 1, pp. 283-292, 1995.
24. M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York: Dover, 1965.
25. M. L. Stein, Interpolation of Spatial Data. New York: Springer-Verlag, 1999.
26. R. Furrer, M. G. Genton, and D. Nychka, "Covariance tapering for interpolation of large spatial datasets," J. Comput. Graph. Stat., vol. 15, no. 3, pp. 502-523, 2006.
27. R. Schaback, "Creating surfaces from scattered data using radial basis functions," in Mathematical Methods in Computer Aided Geometric Design III, M. Daehlen, T. Lyche, and L. Schumaker, Eds. Nashville, TN: Vanderbilt Univ. Press, 1995, pp. 477-496.
28. J. Cortes, S. Martinez, T. Karatas, and F. Bullo, "Coverage control for mobile sensing networks," IEEE Trans. Robot. Auton., vol. 20, no. 2, pp. 243-255, Apr. 2004.
29. L. Guo and S. A. Billings, "State-space reconstruction and spatiotemporal prediction of lattice dynamical systems," IEEE Trans. Autom. Control, vol. 52, no. 4, pp. 622-632, Apr. 2007.