Ronald G. Driggers

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.

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.

NVThermIP modeling of super-resolution algorithms

Undersampled imager performance enhancement has been demonstrated using super-resolution reconstruction techniques. In these techniques, the optical flow of the scene or the relative sub-pixel shift between frames is calculated and a high-resolution grid is populated with spatial data based on scene motion. Increases in performance have been demonstrated for observers viewing static images obtained from super-resolving a sequence of frames in a dynamic scene and for dynamic framing sensors. In this paper, we provide explicit guidance on how to model super-resolution reconstruction algorithms within existing thermal analysis models such as NVThermIP. The guidance in this paper will be restricted to static target/background scenarios. Background is given on the interaction of sensitivity and resolution in the context of a super-resolution process and how to relate these characteristics to parameters within the model. We then show results from representative algorithms modeled with NVThermIP. General guidelines for analyzing the effects of super-resolution in models are then presented.

IR system field performance with superresolution

Superresolution processing is currently being used to improve the performance of infrared imagers through an increase in sampling, the removal of aliasing, and the reduction of fixed-pattern noise. The performance improvement of superresolution has not been previously tested on military targets. This paper presents the results of human perception experiments to determine field performance on the NVESD standard military eight (8)-target set using a prototype LWIR camera. These experiments test and compare human performance of both still images and movie clips, each generated with and without superresolution processing. Lockheed Martins XR® algorithm is tested as a specific example of a modern combined superresolution and image processing algorithm. Basic superresolution with no additional processing is tested to help determine the benefit of separate processes. The superresolution processing is modeled in NVThermIP for comparison to the perception test. The measured range to 70% probability of identification using XR® is increased by approximately 34% while the 50% range is increased by approximately 19% for this camera. A comparison case is modeled using a more undersampled commercial MWIR sensor that predicts a 45% increase in range performance from superresolution.

Sampled imaging sensor design using the MTF squeeze model to characterize spurious response

The sampling limitations associated with staring array imagers cause an aliased signal, or spurious response, that corrupts the image. The spurious response is a function of pre-sample blur, sampling frequency, and post-blur or image reconstruction. Based on data from two NVESD perception experiments, the MTF Squeeze model was developed in order to model the effects of sampling artifacts on target recognition and identification performance. This paper uses MTF Squeeze model to evaluate target acquisition sensor design. A sensitivity analysis is performed where various pre-sample blur and post sample blur spots were considered in order to optimize sensor pre-sample MTF and post-sample MTF for the target recognition and target identification tasks. These results are compared to Schades, Legaults, and Sequinns criteria and suggestions are provided as guidance in sensor design.

Characteristics of infrared imaging systems which benefit from super-resolution reconstruction

There have been numerous applications of super-resolution reconstruction algorithms to improve the range performance of infrared imagers. These studies show there can be a dramatic improvement in range performance when super-resolution algorithms are applied to under-sampled imager outputs. These occur when the imager is moving relative to the target which creates different spatial samplings of the field of view for each frame. The degree of performance benefit is dependent on the relative sizes of the detector/spacing and the optical blur spot in focal plane space. The blur spot size on the focal plane is dependent on the system F-number. Hence, in this paper we provide a range of these sensor characteristics, for which there is a benefit from super-resolution reconstruction algorithms. Additionally, we quantify the potential performance improvements associated with these algorithms. We also provide three infrared sensor examples to show the range of improvements associated with provided guidelines.

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.

Modeling of IR sensor performance in cold weather

Noise in an imaging infrared (IR) sensor is one of the major limitations on its performance. As such, noise estimation is one of the major components of imaging IR sensor performance models and modeling programs. When computing noise, current models assume that the target and background are either at or near a temperature of 300 K. This paper examines how the temperature of the scene impacts the noise in IR sensors and their performance. It exhibits a strategy that can be used to make a 300 K assumption-based model to compute the correct noise. It displays the results of some measurements of signatures of a cold target against a cold background. Range performance of a notional 3rd Gen sensor (midwave IR and long wave IR) is then modeled as a function of scene background temperature.

Superresolution performance for undersampled imagers

The enhancement of undersampled imager performance has been demonstrated using super-resolution techniques. In these techniques, the optical flow of the scene or the relative sub-pixel shifts between frames is calculated and a high resolution grid is populated with spatial data based as a result of scene motion. Performance enhancement has been demonstrated for the case of a static image with the undersampled imager output compared to a static image that has been acquired through a frame series in a dynamic scene. In this research, the performance is compared for four cases: static image with undersampled imager, static image with super-resolution frame sequence, dynamic image with undersampled imager, and dynamic image with super-resolution frame sequence.

Applications of Super-Resolution and Deblurring to Practical Sensors

In image formation and recording process, there are many factors that affect sensor performance and image quality that result in loss of high-frequency information. Two of these common factors are undersampled sensors and sensors blurring function. Two image processing algorithms, including super-resolution image reconstruction and deblur filtering, have been developed based on characterizing the sources of image degradation from image formation and recording process. In this paper, we discuss the applications of these two algorithms to three practical thermal imaging systems. First, super-resolution and deblurring are applied to a longwave uncooled sensor in a missile seeker. Target resolution is improved in the flight phase of the seeker operation. Second, these two algorithms are applied to a midwave target acquisition sensor for use in long-range target identification. Third, the two algorithms are applied to a naval midwave distributed aperture sensor (DAS) for infrared search and track (IRST) system that is dual use in missile detection and force protection/anti-terrorism applications. In this case, super-resolution and deblurring are used to improve the resolution of on-deck activity discrimination.