The sensitivity of active targeting systems in the shortwave infrared band is currently limited by high read noise associated with conventional readout integrated circuitry. This limit imposes a barrier to leveraging other performance trades, such as source power, illumination wavelength, and temporal coherence. Introducing gain in the charge domain prior to signal readout can reduce the impact of read noise, to the point that it no longer limits performance. In preparation for a series of planned active-imaging field tests, we demonstrate improved system performance on a modeling basis with two different charge-domain gain cameras: the electron bombarded active pixel sensor (EBAPS) and the mercury cadmium telluride avalanche photodiode sensor. We find that both solutions mitigate read noise to make either one suitable for laser range gating, but the high dark current associated with EBAPS may make it unsuitable for continuous-wave imaging in some scenarios. These results aid in our understanding of expected performance in field testing of charge-domain gain systems.
Active imaging systems can provide increased contrast-to-noise ratio (CNR) and targeting performance over passive systems in low-light and long-range applications. We use both a radiometric model and the Night Vision Integrated Performance Model to compare the performance of active continuous-wave (CW) and laser range-gated (LRG) imaging systems with laser illumination at 1.6 and 2.1 μm, corresponding to the shortwave infrared (SWIR) and extended SWIR (eSWIR) bands, respectively. The imager performance is characterized by CNR as a function of range, as well as pixels-on-target. The modeling results demonstrate increased performance in the eSWIR band over the SWIR band in the majority of cases and increased performance for LRG systems over CW systems in all cases. The in-progress design of an active imaging testbed to confirm these modeling results with field imagery is discussed.
KEYWORDS: Clouds, Light sources and illumination, Short wave infrared radiation, Near infrared, Cameras, Sun, Atmospheric modeling, Multiple scattering, Signal to noise ratio, Sensors
Daytime low light conditions such as overcast, dawn, and dusk pose a challenge for object discrimination in the reflective bands, where the majority of illumination comes from reflected solar light. In reduced illumination conditions, sensor signal-to-noise ratio can suffer, inhibiting range performance for recognizing and identifying objects of interest. This performance reduction is more apparent in the longer wavelengths where there is less solar light. Range performance models show a strong dependence on cloud type, thickness, and time of day across all wavebands. Through an experimental and theoretical analysis of a passive sensitivity and resolution matched testbed, we compare Vis (0.4-0.7μm), NIR (0.7-1μm), SWIR (1-1.7μm), and eSWIR (2-2.5μm) to assess the limiting cases in which reduced illumination inhibits range performance.
KEYWORDS: Single photon avalanche diodes, Electrons, Histograms, Photons, Systems modeling, Imaging systems, Signal detection, LIDAR, Dark current, Cameras
Single Photon Avalanche Diodes (SPAD) have shown great promise for use in Lidar and low light applications. Although staring arrays were initially developed for medical applications, recent Lidar sensor solution demands have fueled the development of large count staring sensors with quantum efficiencies extending in the NIR/SWIR and with exotic readout circuits. The same technology also enables low light systems with sensitivity below conventional CMOS. As the name implies, SPAD detectors are sensitive to single photons, behave as stochastic devices, and require special treatment for signal interpretation. In this paper, we describe a signal and noise model for both active and passive SPAD based imaging systems that includes the generation of readout events based on the SPAD detector stochastic model. The model presented here allows the evaluation of SPAD based systems under specific illumination conditions and enables the evaluation of SPAD and sensor parameter system sensitivity.
Long-range target identification is well studied in the visible (Vis) and near-infrared (NIR) bands and more recently in the shortwave infrared (SWIR). The longer wavelength of SWIR (1 to 1.7 μm) improves target detection for both long ranges and under challenging atmospheric conditions because it is less limited by scattering and absorption in the atmosphere. For these reasons, SWIR sensors are proliferating on military platforms. The extended shortwave infrared (eSWIR) band spanning from 2 to 2.5 μm is not typically limited by diffraction, and as a result, the band benefits target acquisition both at long ranges and for degraded visual environments (DVEs). Theoretical and experimental data compare eSWIR with Vis, NIR, and SWIR for atmospheric transmission, reflectivity, illumination, and sensor resolution and sensitivity. The experimental setup includes two testbeds, each with four cameras. The first is a wide field of view (FOV) testbed matching FOV at 20 deg for each camera. The second is a narrow FOV telescope testbed to match instantaneous FOV for consistent resolution across all four bands at long ranges. Both the theory and experiment demonstrate advantages of using eSWIR for long-range target identification under DVEs.
Target detection and identification are well-studied problems in the visible and near infrared (IR) bands, with recent work focusing on the short wave IR (SWIR) band. The extended SWIR (eSWIR) band (2 to 2.5 μm) offers an advantage over SWIR due to increased atmospheric transmission, while keeping greater diffraction-limited angular resolution than the midwave IR and longwave IR. eSWIR should additionally improve object-sky contrast due to having lower background sky path radiance than the SWIR. An analysis of the signal-to-noise ratio and contrast for drone imaging in the reflective bands is presented and compared with a Night Vision Integrated Performance Model of drone detection performance using equivalent reflectivities. We find that imaging performance across all four bands is strongly dependent on pixel pitch and contrast.
Long-range target identification is well studied in the Visible (Vis) and near-infrared (NIR) bands, and more recently in the shortwave infrared (SWIR). The longer wavelength of SWIR (1-1.7μm) improves target detection for both long ranges and under challenging atmospheric conditions because it is less limited by scattering and absorption in the atmosphere. For these reasons, SWIR sensors are proliferating on military platforms. The extended shortwave infrared (eSWIR) band spanning from 2 to 2.5μm is not typically limited by diffraction, and, as a result, the band benefits target acquisition both at long ranges and for degraded visual environments. Theoretical and experimental data compare eSWIR to Vis, NIR, and SWIR for atmospheric transmission, reflectivity, illumination, and sensor resolution and sensitivity. The experimental setup includes two testbeds, each with four cameras. The first is a wide field of view (FOV) testbed matching FOV at 20 degrees for each camera. The second is a narrow FOV telescope testbed to match instantaneous FOV (IFOV) for consistent resolution across all four bands at long ranges. Both the theory and experiment demonstrate advantages of using eSWIR for long-range target identification under degraded visual environments.
KEYWORDS: Reflectivity, Short wave infrared radiation, Near infrared, Signal to noise ratio, Cameras, Sensors, Target detection, Atmospheric particles, Visual process modeling, Performance modeling
Target detection and identification are well-studied problems in the visible (VIS) and near infrared (NIR) bands, with recent work focusing on the short wave IR (SWIR) band. The extended SWIR (eSWIR) band (2 to 2.5 μm) offers an advantage over SWIR due to increased atmospheric transmission, while keeping greater angular resolution than the midwave and longwave IR. eSWIR should additionally improve object-sky contrast due to lower background sky path radiance than the SWIR. An analysis of drone signal-to-noise ratio (SNR) and contrast in the reflective bands is presented and compared to an NVIPM model of drone detection performance using equivalent reflectivities.
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