An experiment has been performed at the U.S. Army's Night Vision and Electronic Sensors Directorate to fully test these models. The experiment imagery is intended to test the bounds of the models under which various blur and sampling is representative of the sensor in the task of target identification. The perception experiment is compared to the estimates of performance given by the various models. The model results are then compared and contrasted.

This is a technical historical chronicle of the past and on- going development of performance models for electro-optical sensors carried out by the U.S. Army CECOM NVESD, the original Night Vision Laboratory. The emphasis has been on thermal imaging models and is also the focus of this paper. The origin of the Johnson criteria is shown and the resulting models that have evolved from the original concept proposed by John Johnson. The present formulations of the models are detailed and the newest developments are introduced. The force that drives the various improvements in the models is the development of more sophisticated thermal imagers whose performance must be described and predicted. Background supporting developments in laboratory measurements and field validation are indicated.

In this paper, we studied the imaging effects of pixel spatial-sampling and scan-velocity mismatch in 2D visible image sensors. these effects were examined experimentally by projecting bar pattern sequences of varying spatial frequency on two different devices and by comparing their outputs with the results of a corresponding imaging simulation. Beat patterns and aliased spatial frequencies were observed by imaging the bar pattern sequences onto an area CMOS `active pixel' sensor. Image phase reversal effects were observed by inducing a systematic mismatch between the scan velocity of a bar pattern `sunburst' areal image and the corresponding velocity of the clocked image charge in a time-delay-and-integration CCD image sensor. The visual image effects of an analog-to-digital converter's (ADC) pixel amplitude quantization, specifically integral nonlinearity (INL) and differential nonlinearity (DNL) were studied using two very different input images. The INL and DNL patterns, obtained from measurements on a 14-bit, video ADC were scaled and then imbedded in the response characteristics of these two images. Various scaling of these INL and DNL patterns were used. the results obtained show artifacts varying in impact from insignificant to clearly degrading.

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 Schade's, Legault's, and Sequinn's criteria and suggestions are provided as guidance in sensor design.

Virtual minimum resolvable temperature difference (MRTD) measurements have been performed on an infrared sensor simulation based on FLIR 92 input parameters. By using this simulation , it is possible to perform virtual laboratory experiments on simulated sensors. As part of the validation of this simulation, a series of MRTD experiments were conducted on simulated and real sensors. This paper describes the methodology for the sensor simulation. The experimental procedures for both real and simulated MRTD are presented followed by a comparison and analysis of the results. The utility of the simulation in assessing the performance of current and notional sensors is discussed.

This paper describes an IR sensor model for realistic thermal image synthesis. The model accurately computes the image of a scene provided by an IR sensor, given the incoming radiance from the scene and a number of sensor parameters. This is accomplished by a new modeling approach which allows implementation of computations in better accordance with sensor physics. First thermal flux received by IR sensor detector areas is computed using ray tracing. This implies that the path followed by thermal radiation is retraced through the IR sensor and that sampling is implemented in spatial, spectral and temporal dimensions. Then the conversion of thermal flux into electrical signal is reproduced. This IR sensor model is an improvement over standard IR sensor models. It can account more accurately for many sensor effects: spatial and temporal sampling of the image plane by the detector area, wavelength dependent effects, especially in optical transmittance and detector responsivity, spatially variant effects, such as aberrations and distribution of irradiance on the image plane, and motion of objects in the scene. It can also take into account several other phenomena which are not usually simulated: modification of the distance between optics and detector plane during focusing, geometric distortion and depth of field limitation.

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.

The location and installation of mid-infrared missile warning receiver sensors is limited by the mechanical constraints of the detector/dewar assembly and the associated cryogenic cooler assembly. The size, shape, and weight of these assemblies limit the installation alternatives, and prevent placing the missile warning receiver system in the optimum locations. Hence, their coverage and detection performance is limited. A micro-lens array coupled to a coherent fiber optic bundle and an infrared focal plane array were designed and experimentally implemented, to allow the mid-wave sensor and cryogenic devices to be located remotely from the receiver aperture. This eliminates the receiver aperture placement restrictions while easing the integration and maintenance of the sensor/dewar and cooler. Modulation transfer function and noise equivalent temperature difference measurements were performed to determine the performance of the imaging system.

Generally, Infrared Search and Track systems use linear focal-plane-arrays with time-delay and integration, because of their high sensitivity. However, the readout is a cumbersome process and needs special effort. This paper describes signal processing and hardware (HW) implementation issues related to front-end electronics, non-uniformity compensation, signal formatting, target detection, tracking and display system. This paper proposes parallel pipeline architecture with dedicated HW for computationally intensive algorithms and SW intensive DSP HW for reconfigurable architecture.

The design of remote sensing systems is driven by the need to provide cost-effective, substantive answers to questions posed by our customers. This is especially important for space-based systems, which tend to be expensive, and which generally cannot be changed after they are launched. We report here on the approach we employed in developing the desired attributes of a satellite mission, namely the Multispectral Thermal Imager. After an initial scoping study, we applied a procedure which we call: `End-to-end modeling and analysis (EEM).' We began with target attributes, translated to observable signatures and then propagated the signatures through the atmosphere to the sensor location. We modeled the sensor attributes to yield a simulated data stream, which was then analyzed to retrieve information about the original target. The retrieved signature was then compared to the original to obtain a figure of merit: hence the term `end-to-end modeling and analysis.' We base the EEM in physics to ensure high fidelity and to permit scaling. As the actual design of the payload evolves, and as real hardware is tested, we can update the EEM to facilitate trade studies, and to judge, for example, whether components that deviate from specifications are acceptable.

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model is being developed. The model uses statistical descriptions of class reflectances in a scene and propagates them through the effects of the atmosphere, the sensor, and any processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. The model divides a remote sensing system into three main components: the scene, the sensor, and the processing algorithms. Scene effects modeled include the solar illumination, atmospheric transmittance, shade effects, adjacency effects, and overcast clouds. Sensor effects modeled include the following radiometric noise sources: shot noise, thermal noise, detector readout noise, quantization noise, and relative calibration error. The processing component includes atmospheric compensation, various linear transformations, and a spectral matched filter used to obtain detection probabilities. This model has been developed for the HYDICE airborne imaging spectrometer covering the reflective solar spectral region from 0.4 to 2.5 micrometers . The paper presents the theory and operation of the model, as well as provides the results of validation studies comparing the model predictions to results obtained using HYDICE data. An example parameter trade study is also included to show the utility of the model for system design and operation applications.

We present the Infrared Imaging Spatial Heterodyne Spectrometer (IRISHS) experiment. IRISHS is a new hyperspectral imaging spectrometer for remote sensing being developed by Los Alamos National Laboratory for use in identifying and assaying gases in the atmosphere when viewed against the Earth's background. The prototype instrument, which can operate between 8 and 11.5 micrometers (although the current IR camera operates from 8 - 9.5 micrometers), will be described. Imaging spatial heterodyne spectrometer technology is discussed in four companion papers also presented at this symposium.

We present a preliminary analysis and design framework developed for the evaluation and optimization of infrared, Imaging Spatial Heterodyne Spectrometer (SHS) electro-optic systems. Commensurate with conventional interferometric spectrometers, SHS modeling requires an integrated analysis environment for rigorous evaluation of system error propagation due to detection process, detection noise, system motion, retrieval algorithm and calibration algorithm. The analysis tools provide for optimization of critical system parameters and components including: (1) optical aperture, f-number, and spectral transmission, (2) SHS interferometer grating and Littrow parameters, and (3) image plane requirements as well as cold shield, optical filtering, and focal-plane dimensions, pixel dimensions and quantum efficiency, (4) SHS spatial and temporal sampling parameters, and (5) retrieval and calibration algorithm issues.

A four channel imaging radiometer is now operational as the first sensor on the U.S. Air Force 3.67-meter Advanced Electro Optical System (AEOS) telescope at the Maui Space Surveillance Site on Mt. Haleakala. The four AEOS Radiometer System (ARS) channels cover the visible/near infrared, MWIR (2.0 - 5.5 micrometers ), LWIR (7.9 - 13.2 micrometers ), and VLWIR (16.2 - 23 micrometers ). The bands are separated by dichroic mirrors that direct the visible channel into a cooled enclosure and the infrared channels into a common cryogenic Dewar. Interference filters separate each band into multiple subbands. A novel background suppression technique uses array data and a circular scan generated by the telescope secondary. The ARS design meets challenges in volume constraint on the trunnion, a low vibration cryogenic system, thermal dissipation control, internal calibration, remotely operating four integrated focal plane arrays, high frame rates with their attendant large data handling and processing requirements, and integration into an observatory wide control system. This paper describes the design, integration, and first light test results of the ARS at the AEOS facility.

The development and subsequent production of Cincinnati Electronics Mid Wave Infrared thermal imaging systems has provided a significantly diverse set for system characterization. In this paper we describe measurement methodologies, results and modeled comparisons of the Noise Equivalent Temperature Difference and Minimum Resolvable Temperature Difference characterizations for three systems.

During this presentation, the status of the technology will be described and prototype applications will be demonstrated and discussed. Included in the discussion will be: (1) the ability to distinguish camouflage from the surrounding environment, (2) the ability to see through fog that is opaque to visible imagers, (3) the ability to image eye-safe lasers for range-finding and target-acquisition, and (4) the use in conjunction with NIR flood lights for both covert surveillance and search and rescue operations. The high room-temperature D* makes indium gallium arsenide focal plane arrays excellent candidates for inclusion in small, light-weight, low-power, and low-cost NIR imaging modules. This type of development will enable additional applications such as the use in gun sights and micro-unmanned aerial vehicle surveillance. The presentation will conclude with the discussion of ongoing development activities.

A camera system has been designed using a focal plane array with 320 X 240 pixels. The detector array is based on quantum wells in the GaAs/AlGaAs material system grown onto a GaAs substrate and flipchip mounted to a readout circuit. The camera system uses f-number equals 1.5 optics to create an image of the scene on the FPA. The detector is cooled to approximately 70 K by an integrated Stirling cooler. The system also includes electronics for amplification and analog to digital conversion of the detector signal. The images are either displayed on a monitor or stored in digital format on an integrated hard disk. The short-term temporal noise was measured and the noise equivalent temperature difference was calculated to 16 mK. The spatial noise was found to be comparable to the temporal noise. The properties of the infrared images were valuated with respect to short and long term stability. The stability was found to be very good, giving a high quality image even 1 hour after a calibration. The number of dead pixels was less than 0.1% for several detectors.

This paper reviews some of the technologies associated with evolutionary improvements in imaging infrared seeker designs and predicts technology performance into the future. The impact of technology advances is shown for several components of the missile system, including: dome, optics, motion stabilization, focal plane arrays, analog-to-digital conversions, and computer processing speed.

The Signature Technology Laboratory of the Georgia Tech Research Institute has nearly 10 years experience in the analysis, modeling and simulation of imaging infrared missile seekers. This experience has led to the development of an integrated Imaging Simulation for Infrared Sensors that has been applied to a range of problems from imaging seeker signal processing development to imaging infrared countermeasure concepts exploration. This paper will describe applications of a closed loop model which has the missile seeker signal processor drive the missile gimbal platform line-of-sight, which in turn is used to provide guidance signals to the missile autopilot. The infrared scene generation is briefly described, with emphasis placed on the sensor and signal processor subsystems. Results of test cases are shown, and applications are discussed.

Processing images from an infrared imaging seeker for automatic target detection is an extremely complex issue. Many algorithms exist for image feature enhancement, adaptive thresholding, and track file processing. The authors present an overview of imaging signal processing algorithms, including image processing, image segmentation, and track processing with the intent of highlighting the interrelationship between algorithms and the necessity of evaluating seeker algorithms as a whole. The paper also discusses performance metrics for seeker-intercept geometries and the evaluation of metrics in a closed-loop aircraft-intercept simulation.

Recent progress on the development of 2D staring Focal Plane Array (FPA) sensors has opened up numerous applications ranging from military to commercial. Staring FPA sensors, particularly Infra-Red FPAs, have certain limitations such as non-uniformity, cross-talk and fill-factor. Sensitivity performance of FPAs to these parameters is important particularly for image registration and tracking applications. Each detector in FPA has an active area and dead space around it. The ratio of the active area to the total detector area is called the fill-factor. The dead space around the active detector surface contributes to the loss of information, which in turn can lead to poor performance of the FPA. In this paper, the effect of fill- factor on the performance of the correlation based registration and tracking algorithms is presented. Assuming an input scene, a sampled image output from a FPA is modeled based on a given fill-factor. Sequences of output images resulting from different fill-factors are used for simulation for evaluating the image registration and tracking performance. It is observed that a poor fill-factor results in deteriorated performance.

In mercury-cadmium-telluride (HgCdTe) photoconductive detectors, the recorded signal is usually a nonlinear function of the incident photon flux. This nonlinear behavior must be taken into account for interferometric data transformation process to avoid measurement errors of the incident light intensity. In this work, we investigate the effects of nonlinearity contributed by various factors especially the nonlinearity contributed by the illuminated and non-illuminated detector areas, which to our knowledge was not investigated earlier. The effects of wire resistance and detector preamplifier loading as well as the impact of off-centered detector illumination compared to the centered detector illumination on the nonlinearity are also investigated. Finally, it has been shown that the nonlinearity can be estimated by using a third-order polynomial. Computer simulation results are presented for a constant voltage biased HgCdTe detector to estimate the effect of nonlinearity.

Cincinnati Electronics has developed a small, low power, lightweight thermal imaging system designed for portable, handheld, or OEM applications. The camera will support a 256 X 256 or 320 X 240 MWIR Indium Antimonide (InSb) staring FPA. The sensor is integrated to a high reliability rotary micro-cooler. The camera features include auto level and gain control, fast internal two point non-uniformity correction, a 14 bit data collection port, movable cursor, RS-422 remote operation, and in-field reconfiguration.

The primary objective of a sensor response model is to provide object sighting messages in the form of accurate position and amplitude estimates to the tracker. This paper will examine the capabilities of a sensor response model entitled `Passive Sensor Workbench' to evaluate object sighting measurement accuracy by implementing a selected candidate sensor design and signal/image processing technique. The performance of a sensor response model is also driven by several factors external to the sensor system including the mission, threat, and environment. The mission of the sensor can vary from viewing a target from a surveillance satellite to a seeker onboard an interceptor. The threat viewed from a sensor may vary, from viewing cold targets exoatmospherically, to viewing thrusting boosters against an earth background. The environments to consider include the atmosphere, terrain, clouds, celestial bodies and nuclear effects. Each of these drivers would require a specific sensor design and signal/image processing techniques to perform within specified requirements for acquisition, detection, and track.

We evaluate the error that arises when approximating the generalized Planck's equation with a truncated series of t terms. We develop an analytical estimate for the error of truncating series expansion to t-terms, obtaining integrable and differentiable forms of generalized Planck's equations. The resulting accuracy of better than 1% is obtained with just three terms.

Guidelines to perform Triangle Orientation Discrimination (TOD) measurements are given in the present paper. The optimal range of test pattern sizes and contrasts are specified, as well as the required number of presentations for a threshold estimate. Special attention is paid to the statistical analysis. A standard frequency-of-serving curve is fitted to the observer data in order to obtain 75%- correct thresholds. A (chi) ²-statistic provides an objective criterion for acceptance or rejection of the threshold estimates. Finally, a complete TOD curve is obtained by fitting a weighted least-square polynomial through the 75%-correct thresholds. Further, a simple Go- NoGo screening procedure with objective pass/fail criteria, based on the TOD methodology, is proposed. With the TOD methodology, accurate sensor performance measured and Go- NoGo testing have become very easy to carry out. Therefore, the investment in a thoroughly design measurement setup will apply itself back easily.