Infrared Passive Surveillance for Near Observable Air Targets

This paper proposes a near range, low altitude, target detection, and passive tracking system using a narrow azimuth field of view (AFOV) infrared camera. The technology identifies and tracks infrared-emitting objects such as jet planes and helicopters. Such systems are passive, meaning they do not give out any radiation of their own, unlike radar, so the enemy can’t explore them. This system is used instead of surveillance radars as a gap filler to detect low-flying aircrafts penetrating gaps. Target tracking is often conflicted by the measurement noise and I.R. sensors’ uncertainty. The noise must be illuminated to predict the true path of a moving target. This research analyzes the accuracy of the Extended Kalman Filter (EKF) as a probabilistic prediction method for infrared visual tracking. Various simulations were examined, including maneuvering and non-maneuvering targets. The results show that the Kalman filter, in conjunction with two I.R. searcher cameras, successfully smoothed random deviations from the true path of the targets, enhancing the ability to predict the target trajectory.