The US Army Night Vision and Electronic Sensors Directorate’s Night Vision Integrated Performance Model (NVIPM) is a robust, comprehensive, and sophisticated model that calculates the performance of an infrared imager in the task of target acquisition. The inputs are specifics about the target, atmosphere, optics, detector, electronics, and display and the outputs are probabilities of target detection, recognition, and identification. NVIPM is complicated and takes a good bit of time to master obtaining correct answers based on numerous assumptions and modeling experience. For midwave infrared (MWIR) and longwave infrared (LWIR) sensors, past research has shown that imaging system performance is strongly related to Fλ / d, where F is the f-number, λ is the wavelength, and d is the detector pitch. Fλ / d provides a metric that relates how closely to diffraction-limited performance a sensor operates. We use the past Fλ / d work to develop a simple model that provides probability of target discrimination as a function of range that can be performed with a simple hand calculator or spreadsheet. We compare this model to the NVIPM calculations on 10 very disparate MWIR and LWIR sensors to show robust agreement. We also describe the conditions under which the simple model is valid.
Atmospheric aerosol effects are often overlooked in target acquisition studies. Typically, performance models only consider extinction and turbulence within the prediction processes. The aerosol modulation transfer function (MTF) is included in range acquisition algorithms to determine how scattering and absorption effects change the target identification predictions. We modeled the aerosols as monodisperse water droplets comparable to a tenuous fog or mist. Integrating the aerosol MTF into the system MTF gives the opportunity to utilize the night vision integrated performance model to predict the target identification range with aerosol contributions. The aerosol MTF is a function of range, water droplet composition, wavelength, and aperture size. The analysis focuses on these variables with an emphasis on wavelength dependence to characterize mid-wave and long-wave performance. Results show that the mid-wave systems have a substantial diffraction advantage over long-wave systems. Only in the limit of increasing optical depths do the mid-wave and long-wave performance models begin to converge, verifying that the aerosols can be the limiting factor for target identification.
KEYWORDS: Sensors, Infrared search and track, Target detection, Point spread functions, Modulation transfer functions, Signal to noise ratio, Visibility, Diffraction, Optical engineering, Detector arrays
The performance of an infrared search and track (IRST) sensor depends on a large number of variables that are important for determining systems performance. One of the variables is the pulse visibility factor (PVF). The PVF is linearly related to IRST performance metrics, such as signal-to-noise ratio (SNR) or signal-to-clutter ratio (SCR). Maximizing the performance of an IRST through a smart design of the sensor requires understanding and optimizing the PVF. The resulting peak, average, or worst case PVF may cause large variations in the sensor SNR or SCR as the target position varies in the sensor field of view (FOV) and corresponding position on the focal plane. As a result, the characteristics of the PVF are not straightforward. The definitions and characteristics for the PVF to include ensquared energy (best case PVF), worst case PVF, and average PVF are provided as a function of F * Lambda / dcc (dcc is the center-to-center distance between pixels, i.e., pixel pitch). F * Lambda / dcc is a generalized figure of merit that permits broad analysis of the PVF. We show the PVF trends when the target has a finite size but is still unresolved on the focal plane [smaller than an instantaneous field of view (IFOV)]. The target size was constrained to be no less than 2% of the IFOV but also no greater than 100 % to study the effects on the PVF as a function of target size. Finally, we describe the characteristics of the PVF when optical degradations, such as aberrations, are inherent in the sensor transfer function. The results have illustrated that small F * Lambda / dcc with large fill factor maximized the PVF at the expense of greater variability. Larger F * Lambda / dcc can reduce the PVF variations but results in a decreased PVF. Finite target sizes and additional optical degradation decreased the PVF compared to diffraction-limited systems.
Infrared image quality can be degraded by atmospheric aerosol scattering. Aerosol interactions are dependent upon the atmospheric conditions and wavelength. Measurements of an edge target at range in the LWIR under hot humid weather provided a blur on the image plane, which is characterized by an MTF. In this experiment, the edge spread function, measured at range, was differentiated to obtain the line spread function and transformed into a MTF. By dividing the total measured MTF by the imager and turbulence MTFs, the aerosol MTF was obtained. Numerical analysis performed using MODTRAN and existing known scattering theory was compared with the experimental results. The measured and numerical results demonstrated a significant aerosol MTF suggesting that the aerosol MTF should be included in sensor performance analysis.