A Linear Combination of Heuristics Approach to Spatial Sampling Hyperspectral Data for Target Tracking
Date of Award
Doctor of Philosophy (PhD)
Department of Electrical and Computer Engineering
Michael J. Mendenhall, PhD.
Persistent surveillance of the battlespace results in better battlespace awareness which aids in obtaining air superiority, winning battles, and saving friendly lives. Although hyperspectral imagery (HSI) data has proven useful for discriminating targets, it presents many challenges as a useful tool in persistent surveillance. A new sensor under development has the potential of overcoming these challenges and transforming our persistent surveillance capability by providing HSI data for a limited number of pixels and grayscale video for the remainder. The challenge of exploiting this new sensor is determining where the HSI data in the sensor's field of view will be the most useful. The approach taken is to use a utility function with components of equal dispersion, periodic poling, missed measurements, and predictive probability of association error (PPAE). The relative importance or optimal weighting of the different types of TOI is accomplished by a genetic algorithm using a multi-objective problem formulation. Experiments show using the utility function with equal weighting results in superior target tracking compared to any individual component by itself, and the equal weighting in close to the optimal solution. The new sensor is successfully exploited resulting in improved persistent surveillance.
DTIC Accession Number
Secrest, Barry R., "A Linear Combination of Heuristics Approach to Spatial Sampling Hyperspectral Data for Target Tracking" (2010). Theses and Dissertations. 1428.
Electrical and Electronics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons