A Brownian bridge movement model to track mobile targets

Abstract

The Brownian bridge movement model (BBMM) models target movement between two known points as a Brownian bridge. This thesis extended the BBMM to account for multiple starting and ending points and to account for intelligence inputs midway through the target movement. The BBMM is applied to a military scenario where U.S. forces are conducting surveillance to monitor the breakout of Chinese forces in the South China Sea. Probability heat maps, depicting the probability of a target location at discrete times, are generated through simulations in MATLAB. Using the heat maps, this thesis developed an algorithm to automate the placement of sensors to detect the target. This thesis focused on the use of a network of unmanned sensors as the means for target detection. The relationship between the sensors' attributes and the probability of detection is explored through a meta-experiment. The experiment utilizes a three-stage algorithm that generates heat maps, deploys sensors and randomizes intelligence inputs, and measures the probability of detection. A trade-off analysis was conducted and showed that to achieve a higher probability of detection, it is more effective to have sensors cover a wider area at fewer discrete points in time than to have a greater number of discrete looks using sensors covering smaller areas.