The utilization of a 672 X 544-resistor array based Mobile Infrared Scene Projector (MIRSP) for hardware-in-the- loop test and evaluation of installed imaging infrared (I2R) sensors is presented. The Army US Test and Evaluation Command is developing MIRSP systems for T&E of I2R sensors installed on both aviation and ground platforms. The initial pathfinder MIRSP, discussed here, will be used as a risk-mitigation tool to help determine and define requirements for the objective MIRSP systems. A description of the pathfinder MIRSP configuration, performance characteristics, and operational modes is provided.
The Redstone Technical Test Center (RTTC) has the requirement to project dynamic, infrared (IR) imagery to sensors under test. This imagery must be of sufficient quality and resolution so that, sensors under test will perceive and respond just as they do to real-world scenes. In order to achieve this fidelity from a pixelized infrared resistor emitter array, non-uniformity correction (NUC) is necessary. An important step in performing NUC is to calibrate the IR projection system so as to be capable of projecting a radiometric uniform IR image. The quality of the projected image is significantly enhanced by proper application of this calibration. To properly implement non- uniformity correction, it is necessary to accurately measure the radiometric emission of each element, or display pixel (emitter pixel), in the emitter array. This paper presents mathematical models and image-processing techniques required to successfully calibrate a non-uniform emitter projection system to absolute temperature. RTTC has developed a high- speed, reliable, and flexible means of digitally processing IR images captured from an emitter array. This method of evaluating IR imagery is also useful in performing sensor and overall projection system characterization. The purpose of this paper is to present the methods for correcting the absolute temperature non-uniformity of an IR resistor array.
As part of the Dynamic Infrared Scene Projector (DIRSP) program a new 672 X 544 format suspended membrane microresistor emitter array has been developed. For risk mitigation purposes, the DIRSP arrays were development in phases. In the first phase, a trade-off-analysis and detailed design effort was performed. The second phase followed with the production of a number of DIRSP Engineering Grade Arrays (DEGAs). The second phase included evaluation of DEGAs to determine the need for any design changes for the third and final phase arrays. The third and final phase produced the science grade arrays for the DIRSP program. The DEGAs were the first resistor arrays fabricated using a three level metal CMOS production process. The uncorrected subjective image quality, before application of Non-Uniformity Correction, is significantly better than any pre-existing resistor array known to the authors. A detailed characterization of the spatial, temporal, spectral and radiometric properties of a sample DEGA array is provided here.
A modular cost-effective Infrared Scene Projector (IRSP) system has been designed for testing infrared sensor(s) installed on host aerospace platform(s) in an anechoic chamber environment. The IRSP consists of the following major functional subsystems: Control Electronics Subsystem, Infrared Emitter Subsystem, Projection Optics Subsystem, Mounting Platform Subsystem and Non-Uniformity Correction Subsystem.
Many test facilities currently have the requirement to project dynamic, infrared (IR) imagery into sensors under test. This imagery must be of sufficient quality and resolution so that, sensors under test will perceive and respond just as they do to real-world scenes. In order to achieve this fidelity from an infrared micro-resistor based emitter array, Non-Uniformity Correction (NUC) is necessary. An important step in performing NUC is to calibrate the IR projection system so as to be capable of projecting a uniform temperature/IR image. The quality of the projected image is significantly enhanced by proper application of this calibration. To properly implement non-uniformity correction, it is necessary to accurately measure the IR emissions of each display element, or display pixel (dixel), in the emitter array. Performing these measurements involves collecting a large volume of data at a high rate. The U.S. Army's Test and Evaluation Command (TECOM) has developed a high-speed, relatively inexpensive and flexible means of digitally capturing IR emissions from an emitter array. This method of digitally capturing IR imagery is also useful in performing sensor and overall system characterization. TECOM has investigated, planned, and developed a non-uniformity data collection system, using primarily Commercial Off-The-Shelf (COTS) hardware and software, capable of digitally capturing the emissions of a long wave IR emitter array at 30 frames per second. The digital images are then processed to characterize individual dixels of the IR scene projection system. This paper presents a description of a test facility's need, along with a history of the design, development and actual implementation of a non- uniformity data collection system. In addition to the primary purpose of collecting digital imagery for NUC, other system uses for digital imagery collection are discussed.
Resistor arrays are the leading technology for testing tactical imaging infrared sensors with a real-time Dynamic Infrared Scene Projector (DIRSP) system. The fundamental goal of a DIRSP system is to project `in-band' infrared imagery to a level of detail such that a Unit Under Test (UUT) perceives and responds to the synthesized scenes just as it would to the real world scenes. In the real world, these tactical scenes are continuous functions that contain both low and high spatial frequencies. Unfortunately, resistor arrays have a discrete number of elements requiring a sampled version of the scenario. The output of the DIRSP is a stepwise continuous radiance distribution that is projected through the DIRSP optics, the UUT optics, and onto the UUT detector array. In many sensors, the UUT detector array produces a sampled version of the irradiance. This continuous to digital to continuous to digital system requires careful analysis regarding the aliasing that may result. Results of such an analysis are presented here. Specifically, the aliasing issues are addressed with results obtained for the typical case of a slightly undersampled sensor (regarded in testing as `natural' aliasing). The analysis indicates the scene projector's spatial frequency limit (i.e., its folding frequency) should exceed the average of the UUT sensor' cutoff spatial frequency and the spatial frequency cutoff of the scene pre-filter (or scene band limit if pre-filtering is not used). This constraint does not eliminate aliasing. Rather is provides for the natural aliasing present in the sensor while avoiding spurious effects from unnatural aliasing in the creation and projection of the synthetic tactical scenes. The scene projector requirement developed in this work is applicable for tactical imagers and imaging missile seekers.
Image filtering in sampled dynamic infrared scene projection systems is examined from the point of view of providing an improved insight into the choice of the pixel mapping ratio between the projector and imaging unit-under-test. The 2D vector analysis underlying the transfer of image information in such systems is reviewed and is applied to the dynamic infrared scene projection case. It is shown that the 4:1 (2 X 2:1) pixel mapping ratio previously recommended in a desirable criterion from the spatial fidelity viewpoint, particularly when high spatial frequency information represented by point sources and scene edges is being projected. Cost constraints can, however, prevent the 4:1 mapping ratio from being met, in which case the effects on hardware-in-the-loop simulation validity need to be examined carefully. The vector analysis presented here provides a tool useful for the future examination of such cases.
Present laboratory test techniques for evaluating Forward Looking Infrared (FLIR) target acquisition sensors largely rely on simplistic infrared scenes such as four-bar targets against highly uniform backgrounds. One such test, Minimum Resolvable Temperature (MRT), is the primary laboratory test and evaluation (T&E) parameter for FLIRs. While these `simple' targets remove many `unwanted' variables for engineering analysis, they do not resemble the `real world'. Tactical FLIR sensors are being integrated into target acquisition subsystems (TAS) to provide information for purpose other than visual consumption, including automatic target detection, queuing, tracking, and automatic target recognizers. Ultimately, FLIR TAS operational performance must be demonstrated through live field testing. However, new acquisition strategies are driving toward performance specifications and increased modeling and simulation (and realism) into all levels of the testing processes. The time has come to look beyond MRT to assess the total operational performance of FLIR target acquisition subsystems in the laboratory. This paper describes the application of Dynamic Infrared Scene Projection (DIRSP) to project synthetic in- band infrared imagery (surrogate of the real-world) into the FLIR sensor entrance aperture. This paper concludes with a proposed utilization of DIRSP to support laboratory T&E of tactical FLIR target acquisition subsystems--beyond MRT.
A prototype automated forward looking infrared (FLIR) minimum resolvable temperature difference (MRTD) evaluation software system was developed and tested. After data capture and preliminary image processing of FLIR 4-bar target imagery, the boundary contour system (BCS) model of the human early vision system was coupled with a custom feature extractor to produce a set of features characteristic of those employed by humans during detection tasks. These feature sets, along with known target visibility, were used to train a fuzzy adaptive resonance theory MAP (ARTMAP) decision algorithm to emulate human observer performance in determining MRTD as a function of target to background contrast and target spatial frequency. During prototype system evaluation, the system was trained on 180 pairs of input imagery and human observer response data (resolvable/not-resolvable), and then tested against another 60 input images without the human judgments. The system predictions of human response to the test images were than compared to actual human response decisions for the images. Prototype success rates in the range of 96% to 100% were achieved in correctly predicting human response MRTD decisions in a low fidelity situation.
Stretch and hammer neural networks use radial basis function methods to achieve advantages in generalizing training examples. These advantages include (1) exact learning, (2) maximally smooth modeling of Gaussian deviations from linear relationships, (3) identical outputs for arbitrary linear combination of inputs, and (4) training without adjustable parameters in a predeterminable number of steps. Stretch and hammer neural networks are feedforward architectures that have separate hidden neuron layers for stretching and hammering in accordance with an easily visualized physical model. Training consists of (1) transforming the inputs to principal component coordinates, (2) finding the least squares hyperplane through the training points, (3) finding the Gaussian radial basis function variances at the column diagonal dominance limit, and (4) finding the Gaussian radial basis function coefficients. The Gaussian radial basis function variances are chosen to be as large as possible consistent with maintaining diagonal dominance for the simultaneous linear equations that must be solved to obtain the basis function coefficients. This choice insures that training example generalization is maximally smooth consistent with unique training in a predeterminable number of steps. Stretch and hammer neural networks have been used successfully in several practical applications.
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