Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, April 20-22, 2015
Aircraft detection; Cameras; Electrooptical devices; Extended Kalman filters; Infrared detectors; Nonlinear filtering; Signal detection; Airborne platforms; Detection and tracking; Image tracking; Kinematic parameters; Non-linear filtering problems; Search-and-rescue aircrafts; Sensor fusion; Wide field of view; Signal processing
An airborne EO/IR (electro-optical/infrared) camera system comprises of a suite of sensors, such as a narrow and wide field of view (FOV) EO and mid-wave IR sensors. EO/IR camera systems are regularly employed on military and search and rescue aircrafts. The EO/IR system can be used to detect and identify objects rapidly in daylight and at night, often with superior performance in challenging conditions such as fog. There exist several algorithms for detecting potential targets in the bearing elevation grid. The nonlinear filtering problem is one of estimation of the kinematic parameters from bearing and elevation measurements from a moving platform. In this paper, we developed a complete model for the state of a target as detected by an airborne EO/IR system and simulated a typical scenario with single target with 1 or 2 airborne sensors. We have demonstrated the ability to track the target with 'high precision' and noted the improvement from using two sensors on a single platform or on separate platforms. The performance of the Extended Kalman filter (EKF) is investigated on simulated data. Image/video data collected from an IR sensor on an airborne platform are processed using an image tracking by detection algorithm.
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 947403.