Navy infrared search and track (IRST) systems aim at detecting long range airborne targets closing the naval unit at low altitude above the sea surface.1 The target signal is a 2-D pulse of small spatial extent and is well approximated by the sensor point spread function (PSF); further, the signal to clutter ratio (SCR) is usually very low and detection cannot be performed on a single frame. Multiple frame detection algorithms are therefore used to improve the detection probability. Such algorithms integrate the target signal over a number of consecutive frames by means of appropriate 3-D filters tuned to different target trajectories (velocity or directional filters234). The typical naval surveillance scenario includes background features such as clear sky, clouds, and sea.5 These features are usually nonstationary and their mean value undergoes changes both in space and time. For the integration to be effective, the structures in the IR images must be removed prior to 3-D filtering. This operation, which is called background removal, consists in adaptively estimating the spatially varying mean of the background signal and on subtracting it from each image. Background removal represents a critical step, because even a small error in the background estimate can mask the possible target.
Novel background removal algorithm for navy Infrared Search and Track systems
DIANI, MARCO;CORSINI, GIOVANNI
2001-01-01
Abstract
Navy infrared search and track (IRST) systems aim at detecting long range airborne targets closing the naval unit at low altitude above the sea surface.1 The target signal is a 2-D pulse of small spatial extent and is well approximated by the sensor point spread function (PSF); further, the signal to clutter ratio (SCR) is usually very low and detection cannot be performed on a single frame. Multiple frame detection algorithms are therefore used to improve the detection probability. Such algorithms integrate the target signal over a number of consecutive frames by means of appropriate 3-D filters tuned to different target trajectories (velocity or directional filters234). The typical naval surveillance scenario includes background features such as clear sky, clouds, and sea.5 These features are usually nonstationary and their mean value undergoes changes both in space and time. For the integration to be effective, the structures in the IR images must be removed prior to 3-D filtering. This operation, which is called background removal, consists in adaptively estimating the spatially varying mean of the background signal and on subtracting it from each image. Background removal represents a critical step, because even a small error in the background estimate can mask the possible target.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
