Steve Moyer

Concealed weapon identification using terahertz imaging sensors

Terahertz imaging sensors are being considered for providing a concealed weapon identification capability for military and security applications. In this paper the difficulty of this task is assessed in a systematic way. Using imaging systems operating at 640 GHz, high resolution imagery of possible concealed weapons has been collected. Information in this imagery is removed in a controlled and systematic way and then used in a human observer perception experiment. From the perception data, a calibration factor describing the overall difficulty of this task was derived. This calibration factor is used with a general model of human observer performance developed at the US Army Night Vision and Electronic Sensors Directorate to predict the task performance of observers using terahertz imaging sensors. Example performance calculations for a representative imaging sensor are shown.

Current Infrared Target Acquisition Approach for Military Sensor Design and Wargaming

The U.S. Armys infrared target acquisition models have been used for many years by the military sensor community, and there have been significant improvements to these models over the past few years. Significant improvements are the Target Task Performance (TTP) metric for all imaging sensors, the ACQUIRE-LC approach for low contrast infrared targets, and the development of discrimination criteria for the urban environment. This paper is intended to provide an overview of the current infrared target acquisition modeling approach. This paper will discuss recent advances and changes to the models and methodologies used to: (1) design and compare sensors, (2) predict expected target acquisition performance in the field, (3) predict target detection performance for combat simulations, (4) measure and characterize human operator performance in an operational environment (field performance), and (5) relate the models to target acquisition tasks and address targets that are relevant to urban operations. Finally, we present a catalog of discrimination criteria, characteristic dimensions, and target contrasts.

Probability of identification of small hand-held objects for electro-optic forward-looking infrared systems

This paper describes research on the measurement of the 50% probability of identification cycle criteria (N50,V50) for a set of hand-held objects normally held or used in a single hand. These cycle criteria are used to calibrate the Night Vision Electronic Sensors Directorate (NVESD) target acquisition models. The target set consists of 12 objects, from innocuous to potentially lethal. Objects are imaged in the visible, midwave infrared (MWIR), and long-wave infrared (LWIR) spectrum at 12 different aspects. Two human perception experiments are performed. The first experiment simulates an incremental constriction of the imaging systems modulation transfer function (MTF). The N50, and V50 calibration criteria are measured from this perception experiment. The second experiment not only simulates an incremental constriction of the system MTF but also down samples the imagery to simulate the objects at various ranges. The N50 and V50 values are used in NVTherm 2002 and NVThermIP, respectively, to generate range prediction curves for both the LWIR and MWIR sensors. The range predictions from both NVTherm versions are then compared with the observer results from the second perception experiment. The comparison between the results of the second experiment and the model predictions provides a verification of measured cycle criteria.

Resolvable cycle criteria for identifying personnel based on clothing and armament variations

In the urban environment, it may be necessary to identify personnel based on their type of dress. Observing a police officer or soldier might require a different response than observing an armed civilian. This paper reports on the required number of resolvable cycles to identify different personnel based upon the variations of their clothing and armament. Longwave (LWIR), and midwave infrared (MWIR) images of twelve people at twelve aspects were collected. These images were blurred and 11 human observers performed a 12-alternative forced choice visual identification experiment. The results of the human perception experiments were used to measure the required number of resolvable cycles for identifying these personnel. These results are used in modeling sensor performance tasks and improving war-game simulations oriented to the urban environment.

Identification of handheld objects for electro-optic/FLIR applications

This paper describes research on the determination of the fifty-percent probability of identification cycle criterion (N50) for two sets of handheld objects. The first set consists of 12 objects which are commonly held in a single hand. The second set consists of 10 objects commonly held in both hands. These sets consist of not only typical civilian handheld objects but also objects that are potentially lethal. A pistol, a cell phone, a rocket propelled grenade (RPG) launcher, and a broom are examples of the objects in these sets. The discrimination of these objects is an inherent part of homeland security, force protection, and also general population security. Objects were imaged from each set in the visible and mid-wave infrared (MWIR) spectrum. Various levels of blur are then applied to these images. These blurred images were then used in a forced choice perception experiment. Results were analyzed as a function of blur level and target size to give identification probability as a function of resolvable cycles on target. These results are applicable to handheld object target acquisition estimates for visible imaging systems and MWIR systems. This research provides guidance in the design and analysis of electro-optical systems and forward-looking infrared (FLIR) systems for use in homeland security, force protection, and also general population security.

Rotational clutter metric

A new approach to linear discriminant analysis (LDA), called orthogonal rotational LDA (ORLDA) is presented. Using ORLDA and properly accounting for target size allowed development of a new clutter metric that is based on the Laplacian pyramid (LP) decomposition of clutter images. The new metric achieves correlation exceeding 98% with expert human labeling of clutter levels in a set of 244 infrared images. Our clutter metric is based on the set of weights for the LP levels that best classify images into clutter levels as manually classified by an expert human observer. LDA is applied as a preprocessing step to classification. LDA suffers from a few limitations in this application. Therefore, we propose a new approach to LDA, called ORLDA, using orthonormal geometric rotations. Each rotation brings the LP feature space closer to the LDA solution while retaining orthogonality in the feature space. To understand the effects of target size on clutter, we applied ORLDA at different target sizes. The outputs are easily related because they are functions of orthogonal rotation angles. Finally, we used Bayesian decision theory to learn class boundaries for clutter levels at different target sizes.

Masked target transform volume clutter metric applied to vehicle search

The Night Vision and Electronic Sensors Directorates current time-limited search model, which makes use of the targeting task performance (TTP) metric to describe imager quality, does not explicitly account for the effects of clutter on observer performance. The masked target transform volume (MTTV) clutter metric has been presented previously, but is first applied to the results of a vehicle search perception experiment with simulated thermal imagery here. NVESDs Electro-Optical Simulator program was used to generate hundreds of synthetic images of tracked vehicles hidden in a rural environment. 12 observers searched for the tracked vehicles and their performance is compared to the MTTV clutter level, signal-to-clutter ratios using several clutter metrics from open literature, and to the product of target size and contrast. The investigated clutter metrics included the Schmeider-Weathersby statistical variance, Silks statistical variance, Avirams probability of edge detection metric, and Changs target structural similarity metric. The MTTV was shown to better model observer performance as measured by the perception experiment than any of the other compared metrics, including the product of target size and contrast.

Urban vehicle cycle criteria for identification

In the urban operations (UO) environment, it may be necessary to identify various vehicles that can be referred to as non-traditional vehicles. A police vehicle might require a different response than a civilian vehicle, or a tactical vehicle. This research reports the measured 50% probability of identification cycle criteria (N50s and V50s) required to identify a different vehicle set than previously researched at NVESD. Longwave infrared (LWIR) and midwave infrared (MWIR) imagery of twelve vehicles at twelve different aspects was collected. Some of the vehicles in this confusion set include an ambulance, a police sedan, a HMMWV, and a pickup truck. This set of vehicles represents those commonly found in urban environments. The images were blurred to reduce the number of resolvable cycles. The results of the human perception experiments allowed the 50% probability of identification cycle criteria (N50s and V50s) to be measured. These results will allow the modeling of sensor performance in the urban terrain for infrared imagers.

Direct view optics model for facial identification

Direct view optics is a class of sensors to include the human eye and the human eye coupled to rifle scopes, spotter scopes, binoculars, and telescopes. 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 and the optical magnification. In this research, we extend the direct view model for the application of facial identification. The model is described and the experimental method for calibrating the task of human facial identification is discussed.

Search comparison in the infrared spectra for military operations in the urban terrain

This research compares target detection in the longwave and midwave spectral bands in urban environments. The Night Vision and Electronic Sensors Directorate (NVESD) imaged one hundred scenes at several Army Military Operations in the Urban Terrain (MOUT) sites during day and night. Images were resized to make the field-of-view (FOV) for each scene approximately the same. These images were then presented in a time-limited search perception experiment using military observers. Probabilities of detection were compared between the two spectral bands. Results from MOUT search were compared with previous modeling efforts.