Autonomous control systems: applications to remote sensing and image processing

Details Autonomous control systems: applications to remote sensing and image processing Autonomous control systems: applications to remote sensing and image processing

Nov 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001spie.4471....1j&link_type=abstract

Proc. SPIE Vol. 4471, p. 1-8, Algorithms and Systems for Optical Information Processing V, Bahram Javidi; Demetri Psaltis; Eds.

Mathematics

Logic

Scientific paper

One of the main challenges of any control (or image processing) paradigm is being able to handle complex systems under unforeseen uncertainties. A system may be called complex here if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques cannot easily handle the problem. Examples of complex systems are power networks, space robotic colonies, national air traffic control system, and integrated manufacturing plant, the Hubble Telescope, the International Space Station, etc. Soft computing, a consortia of methodologies such as fuzzy logic, neuro-computing, genetic algorithms and genetic programming, has proven to be powerful tools for adding autonomy and semi-autonomy to many complex systems. For such systems the size of soft computing control architecture will be nearly infinite. In this paper new paradigms using soft computing approaches are utilized to design autonomous controllers and image enhancers for a number of application areas. These applications are satellite array formations for synthetic aperture radar interferometry (InSAR) and enhancement of analog and digital images.

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