Exploration and data refinement via multiple mobile sensors based on Gaussian processes

We consider configuration of multiple mobile sensors to explore and refine knowledge in an unknown field. After some initial discovery, it is desired to collect data from the regions that are far away from the current sensor trajectories to favor the exploration purposes, while simultaneously, exploring the vicinity of known interesting phenomena to refine the measurements. Since the collected data only provide us with local information, there is no optimal solution to be sought for the next trajectory of sensors. Using Gaussian process regression, we provide a simple framework that accounts for both the conflicting data refinement and exploration goals, and to make reasonable decisions for the trajectories of mobile sensors.

[1]  John Gray Peatman,et al.  Introduction to Applied Statistics , 1964, Univariate, Bivariate, and Multivariate Statistics Using R.

[2]  Guillermo Sapiro,et al.  Dictionary Learning for Noisy and Incomplete Hyperspectral Images , 2012, SIAM J. Imaging Sci..

[3]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[4]  Tingting Wu,et al.  Spatial interpolation of temperature in the United States using residual kriging , 2013 .

[5]  Afreen Siddiqi,et al.  Optimal reconfiguration of satellite constellations with the auction algorithm , 2008 .

[6]  D. Beste.  Design of Satellite Constellations for Optimal Continuous Coverage , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Klaus Schilling.  Earth observation by distributed networks of small satellites , 2009, International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009.

[8]  J G Walker,et al.  Continuous Whole-Earth Coverage by Circular-Orbit Satellite Patterns , 1977 .

[9]  Ming-Hsuan Yang,et al.  Online Sparse Gaussian Process Regression and Its Applications , 2011, IEEE Transactions on Image Processing.

[10]  Olivier L. de Weck,et al.  Staged Deployment of Communications Satellite Constellations in Low Earth Orbit , 2004, J. Aerosp. Comput. Inf. Commun..

[11]  Lei Han,et al.  Study on satellite orbit design for boundary environment monitoring , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[12]  Ricardo Píriz,et al.  The Galileo Constellation Design: A Systematic Approach , 2005 .

[13]  G. Kalton,et al.  Introduction to Applied Statistics , 1963 .

[14]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[15]  Farzad Kamalabadi,et al.  Optimal Sensor Array Configuration in Remote Image Formation , 2008, IEEE Transactions on Image Processing.

[16]  M. R. Holdaway,et al.  Spatial modeling and interpolation of monthly temperature using kriging , 2006 .