This PDF file contains the front matter associated with SPIE Proceedings Volume 10178, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

When new and unique task difficulties are requested to be determined, it is important to use methodologies that are consistent with previous research. Unfortunately, some new tasks break the paradigm of past research and require new techniques in order to properly determine their difficulty. This paper describes the process of determining the difficulty for tasks that are unique in that they have a null case (where no object or motion is present) and because these tasks have been requested to be quantified in environments that potentially contain high amounts of atmospheric turbulence. Because each of the calculated V50’s was based upon an assumption, a secondary field collection was necessary in order to validate which model assumptions correlated properly to field performance data.

A known problem with infrared imaging devices is their non-uniformity. This non-uniformity is the result of dark current, amplifier mismatch as well as the individual photo response of the detectors. To improve performance, non-uniformity correction (NUC) techniques are applied. Standard calibration techniques use linear, or piecewise linear models to approximate the non-uniform gain and off set characteristics as well as the nonlinear response. Piecewise linear models perform better than the one and two-point models, but in many cases require storing an unmanageable number of correction coefficients. Most nonlinear NUC algorithms use a second order polynomial to improve performance and allow for a minimal number of stored coefficients. However, advances in technology now make higher order polynomial NUC algorithms feasible. This study comprehensively tests higher order polynomial NUC algorithms targeted at short wave infrared (SWIR) imagers. Using data collected from actual SWIR cameras, the nonlinear techniques and corresponding performance metrics are compared with current linear methods including the standard one and two-point algorithms. Machine learning, including principal component analysis, is explored for identifying and replacing bad pixels. The data sets are analyzed and the impact of hardware implementation is discussed. Average floating point results show 30% less non-uniformity, in post-corrected data, when using a third order polynomial correction algorithm rather than a second order algorithm. To maximize overall performance, a trade off analysis on polynomial order and coefficient precision is performed. Comprehensive testing, across multiple data sets, provides next generation model validation and performance benchmarks for higher order polynomial NUC methods.

Many existing and emerging remote sensing applications in the UV, Visible, NIR, SWIR, MWIR and LWIR regions are challenging the conventional thinking of radiance and temperature calibration techniques. While the relationship between blackbody temperature and optical radiation is well understood, often there is an “invisible” dividing line between treatments of these values as either optical radiance or temperature. It is difficult to perform seamless temperature and radiance calibrations across the point of 2.5um. Spectrum above 2.5um is typically related in temperature terms and below 2.5um may be either spoken of in terms of temperature or optical radiance. There is also a natural unit “convergence” issue at 2.5um, due to the crossover of significant levels of emissivity, reflectance and temperature at this point. NMI traceability in the spectral region of 2.5-14.0um can also be a problem especially for spectral radiance. This paper will outline a possible turn-key test bench solution that provides traceable solutions for both temperature and radiance value in these regimes. The intent of this paper is to offer a possible solution and challenge the infrastructure that exists today over the 0.3-14um range in order to obtain a valid spectral radiance or temperature value, or both, to support emerging sensor fusion technology.

An objective performance of the reflective-band imaging systems is required in order to provide the warfighter with the right technology for a specific task. Various methods to measure and model performance in the visible (Vis) spectral regions have been proposed in the literature. This correspondence shows the influence of the spectral region averaging on the monochromatic modulation transfer function (MTF). This works unequivocally shows that the illumination source plays a crucial role in the accurate predictive analysis of the system performance. For accurate analysis the illumination sources need to be carefully considered for the atmospheric conditions. This work shows the possibility of using an LED configuration in the system performance analysis. Such configurations need rigorous calibration in order to become a valuable asset in system characterization.

This paper examines the measurement of MTF of slant edge targets from airborne imagery. The MTF is calculated by extracting the edge spread function from the slant edge, deriving the line spread function, the performing an FFT to get the MTF. Because characteristics of airborne imagery are not controlled, using edge targets to get the system level MTF present challenges. A method to calculate the MTF from edge targets in airborne imagery is proposed by normalizing the scan lines in the edge spread function and low pass filtering it. An example using air borne imagery is shown and compared with analytical results and laboratory measurements. The paper also examines extracting the effects on the MTF due to image blur from jitter common with air borne imagery.

This paper proposes a read-in integrated circuit (RIIC) for infrared scene projectors, which compensates for the voltage drops in ground lines in order to improve the uniformity of the emitter current. A current output digital-to-analog converter is utilized to convert digital scene data into scene data currents. The unit cells in the array receive the scene data current and convert it into data voltage, which simultaneously self-adjusts to account for the voltage drop in the ground line in order to generate the desired emitter current independently of variations in the ground voltage. A 32 × 32 RIIC unit cell array was designed and fabricated using a 0.18-μm CMOS process. The experimental results demonstrate that the proposed RIIC can output a maximum emitter current of 150 μA and compensate for a voltage drop in the ground line of up to 500 mV under a 3.3-V supply. The uniformity of the emitter current is significantly improved compared to that of a conventional RIIC.

The infrared scene projector (IRSP) is a tool for evaluating infrared sensors by producing infrared images. Because sensor testing with IRSPs is safer than field testing, the usefulness of IRSPs is widely recognized at present. The important performance characteristics of IRSPs are the thermal resolution and the thermal dynamic range. However, due to an existing trade-off between these requirements, it is often difficult to find a workable balance between them. The conventional read-in integrated circuit (RIIC) can be classified into two types: voltage-mode and current-mode types. An IR emitter driven by a voltage-mode RIIC offers a fine thermal resolution. On the other hand, an emitter driven by the current-mode RIIC has the advantage of a wide thermal dynamic range. In order to provide various scenes, i.e., from highresolution scenes to high-temperature scenes, both of the aforementioned advantages are required. In this paper, a hybridmode RIIC which is selectively operated in two modes is proposed. The mode-selective characteristic of the proposed RIIC allows users to generate high-fidelity scenes regardless of the scene content. A prototype of the hybrid-mode RIIC was fabricated using a 0.18-μm 1-poly 6-metal CMOS process. The thermal range and the thermal resolution of the IR emitter driven by the proposed circuit were calculated based on measured data. The estimated thermal dynamic range of the current mode was from 261K to 790K, and the estimated thermal resolution of the voltage mode at 300K was 23 mK with a 12-bit gray-scale resolution.

The development of very-large format infrared detector arrays has challenged the IR scene projector community to develop larger-format infrared emitter arrays. Many scene projector applications also require much higher simulated temperatures than can be generated with current technology. This paper will present an overview of resistive emitterbased (broadband) IR scene projector system development, as well as describe recent progress in emitter materials and pixel designs applicable for legacy MIRAGE XL Systems to achieve apparent temperatures >1000K in the MWIR. These new high temperature MIRAGE XL (LFRA) Digital Emitter Engines (DEE) will be “plug and play” equivalent with legacy MIRAGE XL DEEs, the rest of the system is reusable. Under the High Temperature Dynamic Resistive Array (HDRA) development program, Santa Barbara Infrared Inc. (SBIR) is developing a new infrared scene projector architecture capable of producing both very large format (>2k x 2k) resistive emitter arrays and improved emitter pixel technology capable of simulating very high apparent temperatures. During earlier phases of the program, SBIR demonstrated materials with MWIR apparent temperatures in excess of 1500 K. These new emitter materials can be utilized with legacy RIICs to produce pixels that can achieve 7X the radiance of the legacy systems with low cost and low risk. A 'scalable' Read-In Integrated Circuit (RIIC) is also being developed under the same HDRA program to drive the high temperature pixels. This RIIC will utilize through-silicon via (TSV) and Quilt Packaging (QP) technologies to allow seamless tiling of multiple chips to fabricate very large arrays, and thus overcome the yield limitations inherent in large-scale integrated circuits. These quilted arrays can be fabricated in any N x M size in 512 steps.

ASSET is a physics-based model used to generate synthetic data sets of wide field of view (WFOV) electro-optical and infrared (EO/IR) sensors with realistic radiometric properties, noise characteristics, and sensor artifacts. It was developed to meet the need for applications where precise knowledge of the underlying truth is required but is impractical to obtain for real sensors. For example, due to accelerating advances in imaging technology, the volume of data available from WFOV EO/IR sensors has drastically increased over the past several decades, and as a result, there is a need for fast, robust, automatic detection and tracking algorithms. Evaluation of these algorithms is difficult for objects that traverse a wide area (100-10,000 km) because obtaining accurate truth for the full object trajectory often requires costly instrumentation. Additionally, tracking and detection algorithms perform differently depending on factors such as the object kinematics, environment, and sensor configuration. A variety of truth data sets spanning these parameters are needed for thorough testing, which is often cost prohibitive. The use of synthetic data sets for algorithm development allows for full control of scene parameters with full knowledge of truth. However, in order for analysis using synthetic data to be meaningful, the data must be truly representative of real sensor collections. ASSET aims to provide a means of generating such representative data sets for WFOV sensors operating in the visible through thermal infrared. The work reported here describes the ASSET model, as well as provides validation results from comparisons to laboratory imagers and satellite data (e.g. Landsat-8).

Characterizing an imaging system through the use of linear transfer functions allows prediction of the output for an arbitrary input. Through careful measurement of the systems transfer function, imaging effects can then be applied to desired imagery in order to conduct subjective comparison, image based analysis, or evaluate algorithm performance. The Night Vision Integrated Performance Model (NV-IPM) currently utilizes a two-dimensional linear model of the systems transfer function to emulate the systems response and additive signal independent noise. In this correspondence, we describe how a two-dimensional MTF can be obtained through correct interpolation of one-dimensional measurements. We also present a model for the signal dependent noise (additive and multiplicative) and the details of its calculation from measurement. Through modeling of the experimental setup, we demonstrate how the emulated sensor replicates the observed objective performance in resolution, sampling, and noise. In support of the reproducible research effort, many of the Matlab functions associated with this work can be found on the Mathworks file exchange [1].

We propose a new radiometric unit of measure we call the ‘swux’ to unambiguously characterize scene illumination in the SWIR spectral band between 0.8μm-1.8μm, where most of the ever-increasing numbers of deployed SWIR cameras (based on standard InGaAs focal plane arrays) are sensitive. Both military and surveillance applications in the SWIR currently suffer from a lack of a standardized SWIR radiometric unit of measure that can be used to definitively compare or predict SWIR camera performance with respect to SNR and range metrics. We propose a unit comparable to the photometric illuminance lux unit; see Ref. [1]. The lack of a SWIR radiometric unit becomes even more critical if one uses lux levels to describe SWIR sensor performance at twilight or even low light condition, since in clear, no-moon conditions in rural areas, the naturally-occurring SWIR radiation from nightglow produces a much higher irradiance than visible starlight. Thus, even well-intentioned efforts to characterize a test site’s ambient illumination levels in the SWIR band may fail based on photometric instruments that only measure visible light. A study of this by one of the authors in Ref. [2] showed that the correspondence between lux values and total SWIR irradiance in typical illumination conditions can vary by more than two orders of magnitude, depending on the spectrum of the ambient background. In analogy to the photometric lux definition, we propose the SWIR irradiance equivalent ‘swux’ level, derived by integration over the scene SWIR spectral irradiance weighted by a spectral sensitivity function S(λ), a SWIR analog of the V(λ) photopic response function.

When evaluated with a spatially uniform irradiance, an imaging sensor exhibits both spatial and temporal variations, which can be described as a three-dimensional (3D) random process considered as noise. In the 1990s, NVESD engineers developed an approximation to the 3D power spectral density (PSD) for noise in imaging systems known as 3D noise. This correspondence describes the decomposition of the full 3D PSD into the familiar components from the 3D Noise model. The standard 3D noise method assumes spectrally (spatio-temporal) white random processes, which is demonstrated to be atypically in the case with complex modern imaging sensors. Using the spectral shape allows for more appropriate analysis of the impact of the noise of the sensor. The processing routines developed for this work consider finite memory constraints and utilize Welch's method for unbiased PSD estimation. In support of the reproducible research effort, the Matlab functions associated with this work can be found on the Mathworks file exchange [1].

A key component in any image-based tracking system is the adaptive tracking algorithm used to segment the image into potential targets, rank-and-select the best candidate target, and gate the selected target to further improve tracker performance. Similarly, a key component in any soft-kill response to an incoming guided missile is the flare/chaff decoy used to distract or seduce the seeker homing system away from the naval platform. This paper describes the recent improvements to the naval threat countermeasure simulator (NTCS) of the NATO-standard ship signature model (ShipIR). Efforts to analyse and match the 3D flare particle model against actual IR measurements of the Chemring TALOS IR round resulted in further refinement of the 3D flare particle distribution. The changes in the flare model characteristics were significant enough to require an overhaul to the adaptive track gate (ATG) algorithm in the way it detects the presence of flare decoys and reacquires the target after flare separation. A series of test scenarios are used to demonstrate the impact of the new flare and ATG on IR tactics simulation.

Even the best thermal imagers available today achieve only a fraction of the range performance that is theoretically possible with a given objective lens. Diffraction from the finite aperture of a thermal camera reduces the contrast of high spatial frequencies as well as limiting the maximum spatial frequency in the image. Reclaiming the high frequency contrast can substantially extend range over what is normally thought of as diffraction limited performance. As explained in this paper, the requirements for achieving extended range are 1) small pitch large format focal planes, 2) deep charge well capacities, and 3) intensive deconvolution processing. We will call this combination PWP for pitch, well capacity, and processing which can theoretically increase range performance by a factor of 1.7 for an increase of 70%. In this paper, we also estimate the improved range performance that results from increasing the electron well capacity of long wave infrared cameras. The three technologies needed for a significant advance in thermal imaging are all available today: these include small pixel high density focal planes, deep wells or digital read outs, and digital processors. We hope this paper excites interest in combining those technologies to provide a significant advance in thermal imager performance.

A few image quality metrics for blur assessment have been presented in the last years. However, most of those metrics do not take image noise into account. Yet, image noise is an unavoidable part of the image forming process with digital cameras. Some thermal imagers show larger sensor noise and inhomogeneity compared to cameras operating in the visible range. Further, natural imagery might contain a combination of several degradations. Assessment of degraded images by observer trials is expensive and time consuming. A single robust quality metric might be derived by metrics highly responsive to single degradations and insensitive to others. Hence separate assessment of image blur and noise seems to be reasonable. In this paper we present a deep learning approach for noise-insensitive blur predictions by using Convolutional Neural Networks (CNN) on image patches. In contrast to current blur metrics the model output is highly correlated to blur distortion over a wide range of image noise. The model is trained on images of ImageNet database impaired by Gaussian blur and noise and tested on artificial and natural image data. Local blur estimation based on patches is especially useful for estimation of non-uniform blur due to motion and atmospheric turbulence.

The development of image simulation techniques which map the effects of a notional, modeled sensor system onto an existing image can be used to evaluate the image quality of camera systems prior to the development of prototype systems. In addition, image simulation or `virtual prototyping' can be utilized to reduce the time and expense associated with conducting extensive field trials. In this paper we examine the development of a perception study designed to assess the performance of the NVESD imager performance metrics as a function of fixed pattern noise. This paper discusses the development of the model theory and the implementation and execution of the perception study. In addition, other applications of the image simulation component including the evaluation of limiting resolution and other test targets is provided.

A nighttime surveillance concept consisting of a single surface omnidirectional mirror assembly and an uncooled Vanadium Oxide (VOx) longwave infrared (LWIR) camera has been developed. This configuration provides a continuous field of view spanning 360° in azimuth and more than 110° in elevation. Both the camera and the mirror are readily available, off-the-shelf, inexpensive products. The mirror assembly is marketed for use in the visible spectrum and requires only minor modifications to function in the LWIR spectrum. The compactness and portability of this optical package offers significant advantages over many existing infrared surveillance systems. The developed system was evaluated on its ability to detect moving, human-sized heat sources at ranges between 10 m and 70 m. Raw camera images captured by the system are converted from rectangular coordinates in the camera focal plane to polar coordinates and then unwrapped into the users azimuth and elevation system. Digital background subtraction and color mapping are applied to the images to increase the users ability to extract moving items from background clutter. A second optical system consisting of a commercially available 50 mm f/1.2 ATHERM lens and a second LWIR camera is used to examine the details of objects of interest identified using the panoramic imager. A description of the components of the proof of concept is given, followed by a presentation of raw images taken by the panoramic LWIR imager. A description of the method by which these images are analyzed is given, along with a presentation of these results side-by-side with the output of the 50 mm LWIR imager and a panoramic visible light imager. Finally, a discussion of the concept and its future development are given.

Finding the optimum design is an iterative decision process. Every step in the design process that has conflicting needs requires a trade study. Trade studies indicate which component(s) affect acquisition range the most and the least. The variables include sensor parameters (focal length, aperture diameter, detector size, noise, and viewing distance) and scenario (target size, target/background contrast, line-of-sight jitter, atmospheric transmittance, and atmospheric turbulence). With an almost infinite number of trades possible, selecting the most important requires a priori knowledge of each parameter, the relationship to others, and its effect on acquisition range.

As pixel sizes reduce in the development of modern High Definition (HD) Mid Wave Infrared (MWIR) detectors the interpixel cross-talk becomes increasingly difficult to regulate. The diffusion lengths required to achieve the quantum efficiency and sensitivity of MWIR detectors are typically longer than the pixel pitch dimension, and the probability of inter-pixel cross-talk increases as the pixel pitch/diffusion length fraction decreases. Inter-pixel cross-talk is most conveniently quantified by the focal plane array sampling Modulation Transfer Function (MTF). Cross-talk MTF will reduce the ideal sinc square pixel MTF that is commonly used when modelling sensor performance. However, cross-talk MTF data is not always readily available from detector suppliers, and since the origins of inter-pixel cross-talk are uniquely device and manufacturing process specific, no generic MTF models appear to satisfy the needs of the sensor designers and analysts. In this paper cross-talk MTF data has been collected from recent publications and the development for a generic cross-talk MTF model to fit this data is investigated. The resulting cross-talk MTF model is then included in a MWIR sensor model and the impact on sensor performance is evaluated in terms of the National Imagery Interoperability Rating Scale’s (NIIRS) General Image Quality Equation (GIQE) metric for a range of fnumber/ detector pitch Fλ/d configurations and operating environments. By applying non-linear boost transfer functions in the signal processing chain, the contrast losses due to cross-talk may be compensated for. Boost transfer functions, however, also reduce the signal to noise ratio of the sensor. In this paper boost function limits are investigated and included in the sensor performance assessments.

Due to the large quantity of low-cost, high-speed computational processing available today, computational imaging (CI) systems are expected to have a major role for next generation multifunctional cameras. The purpose of this work is to quantify the performance of theses CI systems in a standardized manner. Due to the diversity of CI system designs that are available today or proposed in the near future, significant challenges in modeling and calculating a standardized detection signal-to-noise ratio (SNR) to measure the performance of these systems. In this paper, we developed a path forward for a standardized detectivity metric for CI systems. The detectivity metric is designed to evaluate the performance of a CI system searching for a specific known target or signal of interest, and is defined as the optimal linear matched filter SNR, similar to the Hotelling SNR, calculated in computational space with special considerations for standardization. Therefore, the detectivity metric is designed to be flexible, in order to handle various types of CI systems and specific targets, while keeping the complexity and assumptions of the systems to a minimum.

ECOMOS is a multinational effort within the framework of an EDA Project Arrangement. Its aim is to provide a generally accepted and harmonized European computer model for computing nominal Target Acquisition (TA) ranges of optronic imagers operating in the Visible or thermal Infrared (IR). The project involves close co-operation of defence and security industry and public research institutes from France, Germany, Italy, The Netherlands and Sweden. ECOMOS uses and combines well-accepted existing European tools to build up a strong competitive position. This includes two TA models: the analytical TRM4 model and the image-based TOD model. In addition, it uses the atmosphere model MATISSE. In this paper, the central idea of ECOMOS is exposed. The overall software structure and the underlying models are shown and elucidated. The status of the project development is given as well as a short outlook on validation tests and the future potential of simulation for sensor assessment.

TRM4 is a commonly used model for assessing device and range performance of electro-optical imagers. The latest version, TRM4.v2, has been released by Fraunhofer IOSB of Germany in June 2016. While its predecessor, TRM3, was developed for thermal imagers, assuming blackbody targets and backgrounds, TRM4 extends the TRM approach to assess three imager categories: imagers that exploit emitted radiation (TRM4 category Thermal), reflected radiation (TRM4 category Visible/NIR/SWIR), and both emitted and reflected radiation (TRM4 category General). Performance assessment in TRM3 and TRM4 is based on the perception of standard four-bar test patterns, whether distorted by under-sampling or not. Spatial and sampling characteristics are taken into account by the Average Modulation at Optimum Phase (AMOP), which replaces the system MTF used in previous models. The Minimum Temperature Difference Perceived (MTDP) figure of merit was introduced in TRM3 for assessing the range performance of thermal imagers. In TRM4, this concept is generalized to the MDSP (Minimum Difference Signal Perceived), which can be applied to all imager categories. In this paper, we outline and discuss the TRM approach and pinpoint differences between TRM4 and TRM3. In addition, an overview of the TRM4 software and its functionality is given. Features newly introduced in TRM4, such as atmospheric turbulence, irradiation sources, and libraries are addressed. We conclude with an outlook on future work and the new module for intensified CCD cameras that is currently under development

The U.S. Army RDECOM CERDEC NVESD MSD’s target acquisition models have been used for many years by the military analysis community for sensor design, trade studies, and field performance prediction. This paper analyzes the results of perception tests performed to compare the results of a field DRI (Detection, Recognition, and Identification Test) performed in 2009 to current Soldier performance viewing the same imagery in a laboratory environment and simulated imagery of the same data set. The purpose of the experiment is to build a robust data set for use in the virtual prototyping of infrared sensors. This data set will provide a strong foundation relating, model predictions, field DRI results and simulated imagery.

Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA’s Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance

The desire to provide the warfighter both ranging and reflected intensity information is increasing to meet expanding operational needs. LIDAR imaging systems can provide the user with intensity, range, and even velocity information of a scene. The ability to predict the performance of LIDAR systems is critical for the development of future designs without the need to conduct time consuming and costly field studies. Performance modeling of a frequency modulated continuous wave (FMCW) LIDAR system is challenging due to the addition of the chirped laser source and waveform mixing. The FMCW LIDAR model is implemented in the NV-IPM framework using the custom component generation tool. This paper presents an overview of the FMCW Lidar, the customized LIDAR components, and a series of trade studies using the LIDAR model.

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.

The U.S. Army RDECOM CERDEC NVESD MSD’s target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks.

This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors’ performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

There is a ubiquitous and never ending need in the US armed forces for training materials that provide the warfighter with the skills needed to differentiate between friendly and enemy forces on the battlefield. The current state of the art in battlefield identification training is the Recognition of Combat Vehicles (ROCV) tool created and maintained by the Communications - Electronics Research, Development and Engineering Center Night Vision and Electronic Sensors Directorate (CERDEC NVESD). The ROC-V training package utilizes measured visual and thermal imagery to train soldiers about the critical visual and thermal cues needed to accurately identify modern military vehicles and combatants. This paper presents an approach that has been developed to augment the existing ROC-V imagery database with synthetically generated multi-spectral imagery that will allow NVESD to provide improved training imagery at significantly lower costs.

Recent developments of long wave infrared (LWIR) devices and LWIR sensor technologies enable us to obtain an LWIR image with high bit depth and low signal-noise ratio. To exploit these recent developments, we propose a novel temperature visualization method that can simultaneously represent global distribution and local details of the input temperature. The global temperature distribution is represented by pseudo color. On the other hand, to manipulate the local temperature details, the output luminance is generated by gradient-domain image reconstruction. Experimental results on real LWIR images show the effectiveness of the proposed method.

In the view of space remote sensing，such as satellite detection，space debris detection etc.，visible band is usually used，in order to have the all-weather detection capability, long wavelength infrared (LWIR) detection is also an important supplement. However, in the tow wave band, the earth can be a very strong interference source, especially in the dim target detecting. When the target is close to the earth, especially the LEO target, the background radiation of the earth will also enter into the baffle, and became the stray light through reflection, the stray light can reduce the signal to clutter ratio (SCR) of the target and make it difficult to be detected. In the visible band, the solar albedo by the earth is the main clutter source while in the LWIR band the radiation of the earth is the main clutter source. So, in this paper, we establish the energy transformation from the earth background radiation to the detection system to assess the effects of the stray light. Firstly, we discretize the surface of the earth to different unit, and using MODTRAN to calculate the radiation of the discrete point in different light and climate conditions, then, we integral all the radiation which can reach the baffle in the same observation angles to get the energy distribution, finally, according the target energy and the non-uniformity of the detector, we can calculate the design requirement of the system stray light suppression, which provides the design basis for the optical system.

Infrared (IR) signal emitted from objects over 0 degree Kelvin has been used to detect and recognize the characteristics of those objects. Recently more delicate IR sensors have been applied for various guided missiles and they affect a crucial influence on object’s survivability. Especially, in marine environment it is more vulnerable to be attacked by IR guided missiles since there are nearly no objects for concealment. To increase the survivability of object, the IR signal of the object needs to be analyzed properly by considering various marine environments. IR signature of a naval ship consists of the emitted energy from ship surface and the reflected energy by external sources. Surface property such as the emissivity and the absorptivity on the naval ship varies with different paints applied on the surface and the reflected IR signal is also affected by the surface radiative property, the sensor’s geometric position and various climatic conditions in marine environment. Since the direct measurement of IR signal using IR camera is costly and time consuming job, computer simulation methods are developing rapidly to replace those experimental tasks. In this study, we are demonstrate a way of analyzing the IR signal characteristics by using the measured background IR signals using an IR camera and the estimated target IR signals from the computer simulation to find the seasonal trends of IR threats of a naval ship. Through this process, measured weather data are used to analyze more accurate IR signal conditions for the naval ship. The seasonal change of IR signal contrast between the naval ship and the marine background shows that the highest contrast radiant intensity (CRI) value is appeared in early summer.

In air-to-air missile applications, it is important to estimate the lock-on distance between the missile and the target by the help of correct radiometric approaches. However, in an air-to-air engagement, due to the dome heating after launch, signal to noise ratio (SNR) decreases and possibility of losing target becomes as a significant issue. Simulations showed that the selection of cut-on and cut-off wavelengths of midwave band pass filters which can be implemented in the optical path of the seeker is very important in order to maintain lock-on during the mission. In this aspect, the critical electro-optical parameters of an air-to-air seeker are investigated before and after the launch.

Mid-wavelength infrared system must correct image before working status. In this paper, we provide a new way to correct image and complete high ratio optical continuous zoom lens design.