Missile approach warning using multi-spectral imagery

Abstract: Man portable air defence systems, MANPADS, pose a big threat to civilian and military aircraft. This thesis aims to find methods that could be used in a missile approach warning system based on infrared cameras. The two main tasks of the completed system are to classify the type of missile, and also to estimate its position and velocity from a sequence of images. The classification is based on hidden Markov models, one-class classifiers, and multi-class classifiers. Position and velocity estimation uses a model of the observed intensity as a function of real intensity, image coordinates, distance and missile orientation. The estimation is made by an extended Kalman filter. We show that fast classification of missiles based on radiometric data and a hidden Markov model is possible and works well, although more data would be needed to verify the results. Estimating the position and velocity works fairly well if the initial parameters are known. Unfortunately, some of these parameters can not be computed using the available sensor data.