We present a conceptual design for an in-situ methane gas sensor that could be deployed rapidly to suspected sites of spurious methane emission. Based on the remote detection of suspected methane leaks now possible with a class of satellites currently in orbit or soon to be launched, in-situ sensors would be deployed to the location of the detection, and accurate measurements of methane leak rates would be reported. Our design is very high level at this time, but incorporates a spectral capability that allows switching on and off the wavelengths of peak methane emission to facilitate detection. Unlike the case for the satellite-based sensing of the gas, our design will lead to the quantification of smallest levels of methane leaks. Two approaches will be considered: ground sensors detecting methane in emission against a cooler sky background, and aerial sensors detecting the gas in absorption against the ground scene as a background source. Sensitivity plays a key role, with the infrared detector working in the 2.4-micron region and operating with near-theoretical sensitivity, with the limiting noise sources set by the background levels for the ground sensor. Consideration of levels of detector dark current, based on the background signal level, and required detector operation temperatures will be derived. The paper reports on the conceptual design, with details on the electronics approach needed to realize the needed levels of sensitivity. Performance quantification will be accomplished through simulation using accurate noise models.
Infrared imagery, like almost any other two-dimensional (2D) imagery, have been traditionally sampled and acquired using a traditional rectangular grid. Therefore, nonuniformity correction (NUC) algorithms for infrared imaging systems which mitigate the most dominant, bias/offset portion of the nonuniformity were developed on the rectangular grid. However, it is well-known that hexagonal sampling grid captures more information in sampled data/imagery when compared to traditional rectangular sampling, and a hexagonal addressing scheme (HAS) for hexagonally-sampled imagery to convert imagery between the two different coordinate systems was developed. In this work, we build on prior work by Sakoglu et al. who developed bilinear interpolation equations between two image frames under the 2-D global motion of the scene or the camera, and apply this 2D algebraic NUC algorithm to hexagonally-sampled imagery directly in the HAS domain by utilizing simulated hexagonal sampling of real IR images.
A diversified infrared technology base has been developed over the recent decades for various civilian and military sensing needs. The technology has been optimized to balance performance and affordability constraints for a variety of end-use goals. Simplistically, these goals might involve the detection and measurement of nearby, bright sources that fill even the largest angular fields-of-view of pixels in simple, low-magnification systems for which abundant signal makes possible infrared detection and measurement with less-sensitive, uncooled sensor arrays. At another extreme are ultra-cryogenically-cooled systems operating below thermoelectric cooler capabilities and which enable the detection and measurement of much fainter sources that underfill even the tiny angular pixel fields of view set by the diffraction limit of large, high magnification optical systems. Our emphasis is closer to the latter for the applications described here. As one example of the environmental monitoring capabilities made possible in the infrared, gas leak detection in transmission pipelines is vitally important for safe operation and for protecting the environment by accounting for and assessing the impact of leaks that adversely affect climate change. Gas leak detection in the infrared spectrum is facilitated by the distinctive spectral fingerprints of fundamental molecular vibrational modes which can be exploited for the detection of the gas. Sensitivity becomes paramount for many applications requiring faint signal detection, and large sensor array formats facilitate surveillance coverage. Many climate change assessments are expected to involve wide-area coverage of Earth scenes with revisit times sufficiently short to capture important transitory events. Shorter term monitoring of containment compliance requires detecting sufficiently small gas leak flows over broad expanses of the Earth’s surface with high detection sensitivities. In this paper we described supporting technologies in the areas of sensor arrays and optical sub-systems, with an emphasis on dispersive spectrometers. There are a plethora of applications involving the stewardship of a range of biological assets, both in the ocean and on land environments, as well as large-scale sensing of atmospheric properties, including concentrations of greenhouse gases.
Previously, Ratliff et al. and Sakoglu et al. developed algebraic nonuniformity correction (NUC) algorithms (the latter developed a matrix-based version with regularization capabilities) which mitigate fixed-pattern nonuniformity (noise) that is notoriously present in infrared image sequences/videos, by utilizing global translational motion of the scene or the imaging camera system. Infrared imagery, like almost any other two-dimensional (2-D) imagery, have been traditionally sampled and acquired using a rectangular grid, therefore the developed NUC algorithms work on this traditional rectangular grid mitigating the most dominant, bias/offset portion of the nonuniformity. On the other hand, it is well-known that hexagonal sampling grid captures more information in sampled data/imagery when compared to traditional rectangular sampling, and a hexagonal addressing scheme for hexagonally-sampled imagery, namely array set addressing scheme, was recently developed by Rummelt et al. in order to be able to convert imagery between the two different coordinate systems and to perform various mathematical and image processing operations. In this work, we derive the bilinear interpolation equations between two image frames for hexagonally-sampled infrared imagery with bias/offset nonuniformity under the 2-D global motion of the scene or the camera, and apply the 2-D algebraic NUC algorithm to hexagonally-sampled imagery. We present a simulation of MWIR infrared imagery with hexagonally-sampled pixel array, with global motion of the scene and with bias/offset nonuniformity, and we test the efficiency of the NUC algorithm on the simulated infrared imagery (based on real MWIR infrared imagery) and compare the performance of the hexagonally-sampled pixel array imagery NUC results to those of the traditional rectangularly-sampled pixel array imagery.
A previous paper described LWIR pupil imaging, and an improved understanding of the behavior of this type of sensor for which the high-sensitivity focal plane array (FPA) operated at higher flux levels includes a reversal in signal integration polarity. We have since considered a candidate methodology for efficient, long-term calibration stability that exploits the following two properties of pupil imaging: (1) a fixed pupil position on the FPA, and (2) signal levels from the scene imposed on significant but fixed LWIR background levels. These two properties serve to keep each pixel operating over a limited dynamic range that corresponds to its location in the pupil and to the signal levels generated at this location by the lower and upper calibration flux levels. Exploiting this property for which each pixel of the Pupil Imager operates over its limited dynamic range, the signal polarity reversal between low and high flux pixels, which occurs for a circular region of pixels near the upper edges of the pupil illumination profile, can be rectified to unipolar integration with a two-level non-uniformity correction (NUC). Images corrected real-time with standard non-uniformity correction (NUC) techniques, are still subject to longer-term drifts in pixel offsets between recalibrations. Long-term calibration stability might then be achieved using either a scene-based non-uniformity correction approach, or with periodic repointing for off-source background estimation and subtraction. Either approach requires dithering of the field of view, by sub-pixel amounts for the first method, or by large off-source motions outside the 0.38 milliradian FOV for the latter method. We report on the results of investigations along both these lines.
A previous paper described LWIR Pupil Imaging with a sensitive, low-flux focal plane array, and behavior of this type of system for higher flux operations as understood at the time. We continue this investigation, and report on a more detailed characterization of the system over a broad range of pixel fluxes. This characterization is then shown to enable non-uniformity correction over the flux range, using a standard approach. Since many commercial tracking platforms include a “guider port” that accepts pulse width modulation (PWM) error signals, we have also investigated a variation on the use of this port to “dither” the tracking platform in synchronization with the continuous collection of infrared images. The resulting capability has a broad range of applications that extend from generating scene motion in the laboratory for quantifying performance of “realtime, scene-based non-uniformity correction” approaches, to effectuating subtraction of bright backgrounds by alternating viewing aspect between a point source and adjacent, source-free backgrounds.
Quantum-dot infrared photodetectors (QDIPs), based on intersubband transitions in nanoscale self-assembled dots, are perceived as a promising technology for mid-infrared-regime sensing since they are based on a mature GaAs technology, are sensitive to normal incidence radiation, exhibit large quantum confined stark effect that can be exploited for hyperspectral imaging, and have lower dark currents than their quantum well counterparts. High detectivity (D* = 1.0E11 cmHz1/2/W at 9 microns) QDIPs have been recently shown to exhibit broad spectral responses approximately 2-micron FWHM) with a bias-dependent shift in their peak wavelengths. This controllable, bias dependent spectral diversity, in conjunction with signal-processing strategies, allows us to extend the operation of the QDIP sensors to a new modality that enables us to achieve: (1) spectral tunability (single- or multi-color) in the range 2-12 microns in the presence of the QDIP's dark current; and (2) multispectral matched filtering in the same range. The spectral tuning is achieved by forming an optimal weighted sum of multiple photocurrent measurements, taken of the object to be probed, one for each bias in a set of prescribed operational biases. For each desired spectral response, the number and values of the prescribed biases and their associated weights are tailored so that the superposition response is as close as possible, in the mean-square-error sense, to the response of a sensor that is optically tuned to the desired spectrum. The spectral matching is achieved similarly but with a different criterion for selecting the weights and biases. They are selected, in conjunction with orthogonal-subspace-projection principles in hyperspectral classification, to nullify the interfering spectral signatures and maximize the signal-to noise ratio of the output. This, in turn, optimizes the classification of the objects according to their spectral signatures. Experimental results will be presented to demonstrate the QDIP sensor's capabilities in these new modalities. The effect of dark current noise on the spectral-tuning capability is particularly investigated. Examples of narrowband and wideband multispectral photocurrent synthesis as well as matched filtering are presented.
The inherent nonuniformity in the photoresponse and readout-circuitry of the individual detectors in infrared focal-plane-array imagers result in the notorious fixed-pattern noise (FPN). FPN generally degrades the performance of infrared imagers and it is particularly problematic in the midwavelength and longwavelength infrared regimes. In many applications, employing signal-processing techniques to combat FPN may be preferred over hard calibration (e.g., two-point calibration), as they are less expensive and, more importantly, do not require halting the operation of the camera. In this paper, a new technique that uses knowledge of global motion in a video sequence to restore the true scene in the presence of FPN is introduced. In the proposed setting, the entire video sequence is regarded as an output of a motion-dependent linear transformation, which acts collectively on the true scene and the unknown bias elements (which represent the FPN) in each detector. The true scene is then estimated from the video sequence according to a minimum
mean-square-error criterion. Two modes of operation are considered. First, we consider non-radiometric restoration, in which case the true scene is estimated by performing a regularized minimization, since the problem is ill-posed. The other mode of operation is radiometric, in which case we assume that only the perimeter detectors have been calibrated. This latter mode does not require regularization and therefore avoids compromising the radiometric accuracy of the restored scene. The algorithm is demonstrated through preliminary results from simulated and real infrared imagery.
Spectrally tunable quantum-dot infrared photodetectors (QDIPs) can be used to approximate multiple spectral responses with the same focal-plane array. Hence, they exhibit the potential for real time adaptive detection/classification. In the present study, it is shown that we can perform the detection/classification operation at the adaptive focal-plane array (AFPA) based on QDIPs by fitting the QDIP's response to the correspondent operators. With a new understanding of spectral signature in the sensor space, the best fitting can be achieved. Our simulation results show how well QDIPs perform in different regions of the spectrum in the mid- and long wave infrared. The results indicate that the AFPA performance does not match that of the ideal filtering operators, but reliable measurement can be accomplished.
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