The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.
Atmospheric turbulence can severely limit the range performance of state-of-the-art large aperture imaging sensor
systems, specifically those intended for long range ground to ground target identification. Simple and cost-effective
mitigation solutions which operate in real-time are desired. Software-based post-processing techniques are attractive as
they lend themselves to easy implementation and integration into the back-end of existing sensor systems. Recently,
various post-processing algorithms to mitigate turbulence have been developed and implemented in real-time hardware.
To determine their utility in Army-relevant tactical scenarios, an assessment of the impact of the post processing on
observer performance is required. In this paper, we test a set of representative turbulence mitigation algorithms on field
collected data of human targets carrying various handheld objects in varying turbulence conditions. We use a controlled
human perception test to assess handheld weapon identification performance before and after turbulence mitigation post-processing.
In addition, novel image analysis tools are implemented to estimate turbulence strength from the scene.
Results of this assessment will lead to recommendations on cost-effective turbulence mitigation strategies suitable for
future sensor systems.
Previous work by the Army's Night Vision and Electronic Sensors Directorate extended the Army's target acquisition
performance models to include the task of facial identification in the visible band. In this work, additional field data for
facial identification is used to validate the direct view optic (DVO) performance model. The target acquisition model for
direct view optics is based on the contrast threshold function of the eye with a modification for the optics modulation
transfer function (MTF) and the optics magnification. We also show that the current Targeting Task Performance (TTP)
metric can approximate the measured data, without using the full accuracy provided by the two-dimensional specific
object method described in previous work.
Real MWIR Persistent Surveillance (PS) data was taken with a single human walking from a known point to different tents in the PS sensor field of view. The spatial resolution (ground sample distance) and revisit rate was varied from 0.5 to 2 meters and 1/8th to 4 Hz, respectively. A perception experiment was conducted where the observer was tasked to track the human to the terminal (end of route) tent. The probability of track is provided as a function of ground sample distance and revisit rate. These results can help determine PS design requirements for tracking and back-tracking humans on the ground. This paper begins with a summary of two previous simulation experiments: one for human tracking and one for vehicle tracking.