Synthetic generation of IRST observations for tracker performance studies
Hector A. Quevedo, Paul F. Singer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
This paper describes an analytic model which generates a synthetic list of detection observations from an IRST. The observation list contains both false detects and target detections. The false detects are generated from a statistical model of the clutter and noise. The user is able to select from a menu of clutter types. This selection determines the values of the statistical parameters. The target type and trajectory are user specified. The target type is selected from a menu and determines the signature of the target. Both the target signature and clutter are propagated through the atmosphere and the sensor. The sensor is modeled as the cascade of transfer functions. The sensor model includes optics, detectors, electronics and noise sources. The signal processing which is part of the sensor model assumes a matched filter is used to increase the S(C + N)R prior to detection. The detection threshold is set to provide the user specified probability of false alarm. Each entry in the observation list includes the observation list includes the observation time, the angular position of the observation, the estimated S(C + N)R of the observation and the number of degrees of freedom which is a measure of clutter severity in the region of the observation. The model is intended to be used as part of a larger simulation for example in a sensor fusion study or to provide tracker test sequences for performance comparison and evaluation.