| Summary: | Thesis (M.Eng.)--Memorial University of Newfoundland, 2009. Engineering and Applied Science Includes bibliographical references (leaves 175-179) This thesis consists of the examination of methodologies for sensor fusion and data fusion of remotely sensed, sparse geospatial targets. Methods for attaining an increased awareness of targets in both tactical and strategic roles are proposed and examined. The example methodologies are demonstrated, and areas for further research noted. Discussions of the proposed methods are carried forth in the context of iceberg detection. -- Amongst the difficulties associated with the combination of sensor parameters and sensor data are the wide variety of technologies, performance ability, coverage, and reliability that are available to those users of remote sensing technology. Typical sensors include airborne search radars, marine search radars, surface wave radar, and satellite synthetic aperture radar. The ability to mitigate the related parametric variances is the test of an appropriate sensor or data fusion algorithm. -- Documented herein are the efforts to find such an algorithm using various statistical methods. Primary among these is Bayes Theorem combined with tracking systems such as the multiple hypothesis tracker. This and other methodologies are explored and evaluated, where appropriate. It will be demonstrated that such a methodology can combine sensor data returns to provide high performance, wide-area, situational awareness with sensors considered to have poor performance.
|
|---|