Richard H. Vollmerhausen

Target acquisition performance modeling of infrared imaging systems: past, present, and future

This paper provides a 40-year review of the infrared imaging system modeling activities at the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD). The result of these modeling activities is a system model that describes the target ac- quisition performance of a human observer and an infrared im- ager. The model has been adopted by the military infrared imaging community as an assessment of how well an ensemble of observers perform the tasks of target detection, recognition, and identifica- tion. The model is used in infrared imager design and assessment, where military users understand how the metrics predicted by the model relates to system performance on the battlefield. This review begins with early work in the late 1950s and proceeds to present day modeling successes. Finally, the infrared imaging system mod- eling activities for the future are discussed. Index Terms—Infrared imaging, modeling.

Modeling the target acquisition performance of active imaging systems

Recent development of active imaging system technology in the defense and security community have driven the need for a theoretical understanding of its operation and performance in military applications such as target acquisition. In this paper, the modeling of active imaging systems, developed at the U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate, is presented with particular emphasis on the impact of coherent effects such as speckle and atmospheric scintillation. Experimental results from human perception tests are in good agreement with the model results, validating the modeling of coherent effects as additional noise sources. Example trade studies on the design of a conceptual active imaging system to mitigate deleterious coherent effects are shown.

Third generation imaging sensor system concepts

Second generation forward looking infrared sensors, based on either parallel scanning, long wave (8 - 12 um) time delay and integration HgCdTe detectors or mid wave (3 - 5 um), medium format staring (640 X 480 pixels) InSb detectors, are being fielded. The science and technology community is now turning its attention toward the definition of a future third generation of FLIR sensors, based on emerging research and development efforts. Modeled third generation sensor performance demonstrates a significant improvement in performance over second generation, resulting in enhanced lethality and survivability on the future battlefield. In this paper we present the current thinking on what third generation sensors systems will be and the resulting requirements for third generation focal plane array detectors. Three classes of sensors have been identified. The high performance sensor will contain a megapixel or larger array with at least two colors. Higher operating temperatures will also be the goal here so that power and weight can be reduced. A high performance uncooled sensor is also envisioned that will perform somewhere between first and second generation cooled detectors, but at significantly lower cost, weight, and power. The final third generation sensor is a very low cost micro sensor. This sensor can open up a whole new IR market because of its small size, weight, and cost. Future unattended throwaway sensors, micro UAVs, and helmet mounted IR cameras will be the result of this new class.

Atmospheric turbulence modulation transfer function for infrared target acquisition modeling

A new direction for the US Army Night Vision and Electronic Sensors Directorate is the development of ultra-narrow field of view (UNFOV) infrared target acquisition (TA) systems. Frequently, the per- formance of these systems is limited by atmospheric turbulence in the imaging path. It is desirable to include the effects of atmospheric turbu- lence blur in infrared TA models. The current TA models are currently linear shift invariant (LSI) systems with component modulation transfer functions (MTFs). The use of additional MTFs, to account for atmo- spheric turbulence, requires that the turbulence blur have LSI properties. The primary unresolved issue with the treatment of turbulence blur as an MTF is the LSI characteristics of the blur. Significant variation in spatial blur and temporal blur prohibit the use of a single MTF in an LSI target acquisition model. Researchers at Ben-Gurion University (BGU) use a TA model that includes an LSI blur, which is a temporal average of the turbulence blur. The research described here evaluates the BGU-type treatment of atmospheric MTF and determines it reasonable for inclusion in the US Armys TA model. In addition to the spatial characteristics, the temporal variation of the turbulence blur is also described.

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.

Target identification performance as a function of temporal and fixed pattern noise

With the increased interest and use of staring IR focal plane arrays, the characterization of fixed pattern noise in task performance is becoming more important. Past work includes theoretical treatments and laboratory measurements to describe the characteristics of fixed pattern noise on target acquisition performance. This is the first target acquisition experiment that describes the relative effects of fixed pattern noise and temporal noise on target identification. Static IR tank images are processed with six different levels of fixed pattern noise and six different levels of temporal noise. A perception experiment is performed where 10 U.S. Army soldiers were tasked to identify the tanks through the combinations of noise. Additive noise was applied in both Gaussian and uniform distributions. The results enable a direct comparison between the effects of fixed pattern noise and temporal noise on target identification.

Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation

The recognition of objects using target acquisition systems is modeled by a sensors minimum resolvable temperature (MRT), the Johnson criteria, atmospherics, and object specifics. Collectively, these three characteristics provide an acquisition model for estimating the probability of object recognition (and detection, identification) as a func- tion of sensor-to-object range. This technique is called the probabilities of discrimination. When quantifying the performance of intelligence- surveillance-reconnaissance (ISR) systems, object recognition is as- sessed using the National Imagery Interpretability Scale (NIIRS). Each NIIRS level corresponds to a different capacity for object recognition and is defined by a set of recognition criteria. The general image quality equation (GIQE) is the ISR sensor model that determines the expected NIIRS level of a sensor for a given set of sensor parameters. It is impor- tant that electro-optical sensor engineers understand both of these rec- ognition models. The segregation between the target acquisition and ISR sensor communities is becoming less sharp as ISR sensors are begin- ning to be used for target acquisition purposes and visa versa. Network and wireless communication advances are providing the means for dual exploitation of these systems. Descriptions of these two recognition models, probabilities of discrimination, and the GIQE are provided. The two models are applied to example systems. Finally, the two models are compared and contrasted.

Modeling target acquisition tasks associated with security and surveillance

Military sensor applications include tasks such as the surveillance of activity and searching for roadside explosives. These tasks involve identifying and tracking specific objects in a cluttered scene. Unfortunately, the probability of accomplishing these tasks is not predicted by the traditional detect, recognize, and identify (DRI) target acquisition models. The reason why many security and surveillance tasks are functionally different from the traditional DRI tasks is described. Experiments using characters and simple shapes illustrate the problem with using the DRI model to predict the probability of identifying individual objects. The current DRI model is extended to predict specific object identification by including the frequency spectrum content of target contrast. The predictions of the new model match experimental data.

Atmospheric modulation transfer function in the infrared

In high-resolution ultranarrow field-of-view thermal imagers, image quality over relatively long path lengths is typically limited by atmospheric degradation, especially atmospheric blur. We report our results and analyses of infrared images from two sites, Fort A. P. Hill and Aberdeen Proving Ground. The images are influenced by the various atmospheric phenomena: scattering, absorption, and turbulence. A series of experiments with high-resolution equipment in both the 3-5- and 8-13-microm regions at the two locations indicate that, as in the visible, image quality is limited much more by atmosphere than by the instrumentation for ranges even of the order of only a few kilometers. For paths close to the ground, turbulence is more dominant, whereas for paths involving higher average elevation, aerosol modulation transfer function (MTF) is dominant. As wavelength increases, turbulence MTF also increases, thus permitting aerosol MTF to become more dominant. A critical role in aerosol MTF in the thermal infrared is attributed to absorption, which noticeably decreases atmospheric transmission much more than in the visible, thereby reducing high-spatial-frequency aerosol MTF. These measurements indicate that atmospheric MTF should be a basic component in imaging system design and analysis even in the infrared, especially as higher-resolution hardware becomes available.

Target Detection Threshold in Noisy Color Imagery

Current target acquisition models are for monochrome imagery systems (single detector). The increasing interest in multispectral infrared systems and color daylight imagers highlights the need for models that describe the target acquisition process for color systems (2 or more detectors). This study investigates the detection of simple color targets in a noise color background.