Concepts in optimal search theory have been used in human-based aerial search since World War II. This thesis addresses the technical and theoretical issues necessary to apply this crucial theory to search path planning for Small Unmanned Aerial Vehicles (SUAVs). A typical search often requires that more than one target be located. Accordingly, a method is presented to locate multiple targets in three dimensions, as well as to differentiate between them. However, significant error can be present when locating targets from an airborne platform, and the idea of target quality is also introduced as a way to describe the reliability of target estimates. Flight test results are presented to validate the target differentiation algorithm. In this test, five out of six targets as close as 6.1 m apart are located and differentiated with less than four meters of error. This flight test also provides color information that is useful in generating artificial target images and understanding the target detection probability. Image skew is then incorporated into the detection probability model, and a function is derived that predicts target detection as a function of distance. In order to measure the effectiveness of search algorithms with this model, the concept of a probability map is introduced. This map can be updated as the search progresses, and is stored on a probability grid containing nodes that keep track of the probable target locations and the probability of detection. Using this tool, a search width is developed for a given airborne agent. The search width is then used to derive optimal search performance based on a given probability map and SUAV. Finally, the concepts of efficiency and completeness are given specific definitions in the context of discrete search. These metrics are used to develop a search plan that focuses on efficiency, and one that focuses on completeness. Example simulations are used to illustrate the conditions under which each plan might be desirable, and a composite search strategy is presented that combines both plans.



College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering



Date Submitted


Document Type





BYU, UAV, agent, unmanned, target, localization, optimal, search, rescue, camera, image, skew, calibration, detection, probability, contour, greedy, lateral, range, width, color, segmentation, point, spread, map, Koopman, Stone, Zamboni