TARGET TRACKING: IMPLEMENTING THE KALMAN FILTER

Prakhar Agarwal, David Federman, Kevin Ge, Tzu-Han Jan, Calvin Jones, Yihua Lou, Joshua Ma, Shyam Modi, Kevin Shi, Vanessa Tan, Melissa Zhang

Advisor: Randy Heuer
Assistant: Zack Vogel

ABSTRACT

Target tracking is often complicated by the measurement noise. The noise must be “filtered” out in order to predict the true path of a moving target. In this study of linear filtering, the Kalman filter, a recursive linear filtering model, was used to estimate tracks. Various situations were examined, including maneuvering targets, multiple radars, multiple targets, and collision avoidance. Based on the results, the Kalman filter was successful in smoothing random deviations from the true path of the targets, improving in its ability to predict the path of each target as more measurements from the tracker were processed.