This paper discusses selected aspects of an MIT Lincoln Laboratory effort developing information fusion techniques for
biodefense decision-support tasks, involving biological standoff (lidar - light detection and ranging) sensors,
meteorology, as well as point sensors and potentially other battlespace sensing and contextual information. The
Spatiotemporal Coherence (STC) fusion approach developed in this effort combines phenomenology aspects with
approximate uncertainty measures to quantify corroboration between the information elements. The results indicate that
STC can significantly reduce false alarm rates. Meandering Plume and Background Simulation is one of two techniques
developed for ground-truth data generation. Beyond the detection realm, developed techniques include information-fusion
based plume mapping and propagation prediction.
This paper discusses some of the techniques developed at MIT Lincoln Laboratory for information fusion of lidar-based
biological standoff sensors, meteorology, point sensors, and potentially other information sources, for biodefense
applications. The developed Spatiotemporal Coherence (STC) fusion approach includes phenomenology aspects and
approximate uncertainty measures for information corroboration quantification. A supervised machine-learning
approach was also developed. Computational experiments involved ground-truth data generated from measurements and
by simulation techniques that were developed. The fusion results include performance measures that focus explicitly on
the fusion algorithms' effectiveness. Both fusion approaches enable significant false-alarm reduction. Their respective
advantages and tradeoffs are examined.
The JPEO-CBD, in conjunction with other members of the defense community, is actively assessing future system architectures for Major Defense Acquisition Programs (MDAPs) such as the Future Combat Systems (FCS). Sensors, networks and information superiority are key elements of FCS, and the JPEO will provide the critical capability that enables complete situational awareness of CB hazards. After a brief overview of the JPEO-CBD program, this paper discusses early insight into how FCS operations and platforms affect CB sensors. Key challenges will be highlighted, such as sensor/platform integration, broad spectrum detection, and sensor performance.
Detection performance of LWIR passive standoff chemical agent sensors is strongly influenced by various scene parameters, such as atmospheric conditions, temperature contrast, concentration-path length product (CL), agent absorption coefficient, and scene spectral variability. Although temperature contrast, CL, and agent absorption coefficient affect the detected signal in a predictable manner, fluctuations in background scene spectral radiance have less intuitive consequences. The spectral nature of the scene is not problematic in and of itself; instead it is spatial and temporal fluctuations in the scene spectral radiance that cannot be entirely corrected for with data processing. In addition, the consequence of such variability is a function of the spectral signature of the agent that is being detected and is thus different for each agent. To bracket the performance of background-limited (low sensor NEDN), passive standoff chemical sensors in the range of relevant conditions, assessment of real scene data is necessary1. Currently, such data is not widely available2. To begin to span the range of relevant scene conditions, we have acquired high fidelity scene spectral radiance measurements with a Telops FTIR imaging spectrometer3. We have acquired data in a variety of indoor and outdoor locations at different times of day and year. Some locations include indoor office environments, airports, urban and suburban scenes, waterways, and forest. We report agent-dependent clutter measurements for three of these backgrounds.
A systems analysis framework for assessing performance of long wave infra-red (LWIR) hyperspectral chemical imaging sensors (HCIS) is presented. The trade space study includes assessment of HCIS detection sensitivity and deployment impact on meeting specified mission requirements.
In order to meet current and emerging needs for remote passive standoff detection of chemical agent threats, MIT Lincoln Laboratory has developed a Wide Area Chemical Sensor (WACS) testbed. A design study helped define the initial concept, guided by current standoff sensor mission requirements. Several variants of this initial design have since been proposed to target other applications within the defense community. The design relies on several enabling technologies required for successful implementation. The primary spectral component is a Wedged Interferometric Spectrometer (WIS) capable of imaging in the LWIR with spectral resolutions as narrow as 4 cm-1. A novel scanning optic will enhance the ability of this sensor to scan over large areas of concern with a compact, rugged design. In this paper, we shall discuss our design, development, and calibration process for this system as well as recent testbed measurements that validate the sensor concept.
We consider the problem of remotely identifying gaseous materials using passive sensing of long-wave infrared (LWIR) spectral features at hyperspectral resolution. Gaseous materials are distinguishable in the LWIR because of their unique spectral fingerprints. A sensor degraded in capability by noise or limited spectral resolution, however, may be unable to positively identify contaminants, especially if they are present in low concentrations or if the spectral library used for comparisons includes materials with similar spectral signatures. This paper will quantify the relative importance of these parameters and express the relationships between them in a functional form which can be used as a rule of thumb in sensor design or in assessing sensor capability for a specific task.
This paper describes the simulation of remote sensing datacontaining a gas cloud.In each simulation, the spectra are degraded in spectral resolution and through the addition of noise to simulate spectra collected by sensors of varying design and capability. We form a trade space by systematically varying the number of sensor spectral channels and signal-to-noise ratio over a range of values. For each scenario, we evaluate the capability of the sensor for gas identification by computing the ratio of the F-statistic for the truth gas tothe same statistic computed over the rest of the library.The effect of the scope of the library is investigated as well, by computing statistics on the variability of the identification capability as the library composition is varied randomly.
Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.
The Geostationary Operational Environmental Satellite (GOES) platform carries an infrared atmospheric sounding instrument which is used to obtain vertical profiles of atmospheric temperature and humidity throughout much of the western hemisphere. These profiles are numerically retrieved from measured nadir-viewing spectral radiances. The opacity of clouds to IR radiance makes such instruments functional only in clear-air regions. Because severe weather is associated with clouded regions, it is highly desirable to obtain soundings through holes in the cloud cover and up to the edge of frontal boundaries. There is much difficulty in performing this task with the existing GOES sounder because cloud cover gives rise to radiance errors in adjacent, and more distant, clear-air fields-of-view. A primary cause for this problem is diffraction, which introduces optical crosstalk between fields-of-view, and which is exacerbated by the large radiance contrast between clouds and clear air. This paper describes a novel application of tapered, or apodized, aperture illumination which may be employed in future GOES sounding instruments to mitigate the effects of diffraction. Tapering the aperture illumination at the edges (or applying this taper at accessible pupils, which are images of the aperture stop) reduces the subsidiary rings of the point-spread function. The benefits of pupil apodization are quantified, as are the penalties incurred by effectively making the aperture smaller. The construction of a graded-transmission spatial filter is described, and its optimal location in a sounding instrument based on a Michelson spectrometer is defined. Finally, the results of measurements taken on a fabricated filter are presented.
We present the pre-launch infrared calibration of the Geostationary Operational Environmental Satellite (GOES) I-M Imager and Sounder. In addition to contractual performance verification, pre-launch calibration provides necessary information for on-orbit operations. These are system relative spectral response, non-linearity in radiometric response and verification of the accuracy of the on board calibration source. The JR channels are calibrated in a thermal vacuum chamber, under varying instrument operating conditions, with two external, temperature controlled blackbodies. A LN2 controlled target represents cold space, and a variable (200 K to 320 K) temperature target represents the Earth scene. We show methods and results for the following instrument performance parameters: system spectral response, noise, non-linearity and relative accuracy. As there is no absolute radiometric standard, the relative accuracy estimates are between the internal (used for on orbit operational calibration) and external calibration sources. Performance trends versus instrument operating condition and across serial number (SNO3-SNO7) are highlighted. We show residual calibration anomalies and describe probable causes.
The imagers and sounders aboard NOAA's Geostationary Operational Environmental Satellites (GOES) provide quantitative data for weather forecasting and studies of the Earth's atmosphere and surface. This paper describes the post-launch radiometric testing of the imagers and sounders and present result from the instruments aboard the GOES-8, - 9, and -10 satellites. In these tests, we measure such quantities as nose and signal-to-noise ratios, radiometric responsivities and their variability, and detector 1/f noise. Performance anomalies specific to GOES, such as variation of scan-mirror reflectance with east-west scan angle, are characterized. The on-orbit results are compared with the performance specification, with pre-launch test result, and with results, and with results of post-launch tests of the imagers and sounders on other GOES satellites.
The Emergency GOES Imager study responds to the potential need for a small, back-up imager for weather observations in the event of failure of one or more of the current GOES satellites. The Emergency GOES Imager (EGI) is designed to be compact and lightweight. Minimal spatial resolution is required in the visible and IR band for the purpose of synoptic forecasts. The ground resolution requirement is 16 km for the 10.2 to 11.2 micrometers IR band and 4 km for the 0.5 to 0.7 micrometers visible band. Due to the small size of the instrument, the EGI has the potential to be deployed either alone on a small launcher or as an auxiliary payload on a larger satellite. The overall size of the EGI is dependent on the orientation of the satellite because of the dependence on amount of solar shielding required for the cooler, and the choice of coolers for specific satellite orientations. Although the EGI design is for an emergency system, the design utilizes recent technology in the form of both a linear IR focal plane array, in front of its constant-motion mirror, and a visible CCD array with a staring-format. The IR array has the potential to present a technical challenge to array manufacturers in the area of calibration, assuming a 0.1 K NEDT. We discuss the means by which the emergency requirements are met with this small and simple system, define the limiting technologies in the design, and explore modifications necessary to expand these requirements.
Understanding the sources of uncertainty in GOES Imager IR data is important to meteorologists and scientists who develop meteorological products. One component of radiometric uncertainty that is not well characterized, unlike noise and calibration errors, arises from the sensors's MTF. To understand this effect it is necessary to know the amount of power at high spatial frequencies in a typical scene. The sensor MTF, however, acts as a lower pass filter on the scene spatial frequency content, passing low frequencies and attenuating higher frequencies. To study the effect of the higher spatial frequencies in a scene, a model of both sensor MTF and scene spatial frequency content has been developed. The scene model is based on data from the Modis Airborne Simulator (MAS), a 50 channel radiometer- imager flown aboard a NASA ER-2. The MAS sensor has a 50 m IFOV at nadir, compared to the GOES channel radiometer- imaging flown aboard a NASA ER-2. The MAS sensor has a 50 m IFOV at nadir, compared to the GOES 4 km IFOV. The data sets from which the scene model was developed contain various combinations of land and clouds from several flights. Overlapping power spectral densities from the two sensors validate the use of MAS data for a GOES scene model at high spatial frequencies. The sensor MTF model is based both on measurements made during pre-launch testing and on theoretical calculations from sensor f-number, detector size and electronics filtering. The MTFs of Imager channels 2 and 4 are compared. Their difference is applied to the scene power spectra to evaluate the average radiometric error due to MTF differences.
Pre-launch instrument calibration testing of the current series of GOES Imagers and Sounders has shown a systematic non-linearity in the radiometric response of the SWIR channels of approximately 0.2 percent. Possible mechanisms for the observed non-linearity include; inherent InSb detector non-linearity, non-constant temperature error on the blackbody calibration target, relative spectral response error, and electronics non-linearity. A calibration model common to the GOES and POES radiometers is presented. The sensitivity of the SWIR channels' non-linear response to errors in spectral response is derived. Systematic shift of spectral response center wavenumber of approximately 10 cm-1 are found to induce a 0.1 percent non-linearity in response. Pre-launch calibration coefficients from recent NOAA radiometers are analyzed. Operational calibration errors caused by incorrect quadratic coefficients are found to be as large as 0.15K for warm scenes.
The present GOES imager exhibits East-West stripes in IR images due to low frequency errors in the calibration of the adjacent North-South detectors. Striping makes delineating boundaries of structures in images difficult, especially in the case of cold scenes. A computer program has been developed that generates simulated IR images using detector noise parameters as inputs. The simulation includes errors due to background drift between space clamps, drift during a space clamp, and errors determining the first order gain during the internal blackbody calibration. The results of the simulation agree well with on orbit measurements of GOES 8 and 9 striping in channels 4 and 5. These simulations can also predict striping performance of future GOES imagers from detector noise parameters allowing for improved detector selection constraints.
Background phenomenology databases and models are essential for the design and assessment of electro-optical sensing systems. The MWIR band has been proposed to satisfy a number of specific requirements in the DoD space based mission areas. However, the phenomenology database in the MWIR to support the design and performance evaluation is limited. Currently the high resolution infrared radiation sounder (HIRS/2) onboard NOAA 12, an operational polar orbiting environmental and weather satellite, offers continual global coverage of several bands in the MWIR. In particular, Channel 17 operates in the heart of the 4.23 micrometer carbon-dioxide band. Though with coarse resolution (approximately 20 km), the vast database offers a good baseline understanding of the MWIR phenomenology related to space based MWIR systems on (1) amplitude variation as function of latitude, season, and solar angle, (2) correlation to relevant MWIR features such as high-altitude clouds, stratospheric warming, aurora and other geomagnetic activities, (3) identification of potential low spatial frequency atmospheric features, and (4) comparison with future dedicated measurements. Statistical analysis on selected multiple orbits over all seasons and geographical regions was conducted. Global magnitude and variation in these bands were established. The overall spatial gradient on the 50 km scale was shown to be within sensor noise; this established the upper bound of spatial frequency in the heart-of-the-carbon-dioxide-band. Results also compared favorably with predictions from atmospheric background models such as the Synthetic High Altitude Radiance Code (SHARC-3).