Proceedings Article | 6 July 1994
KEYWORDS: Sensors, 3D modeling, Performance modeling, Filtering (signal processing), Signal processing, Signal to noise ratio, Atmospheric modeling, 3D acquisition, Clouds, Monte Carlo methods
A scenario-based model has been developed to predict performance of infrared imaging sensors, including optimal and suboptimal processing gains from filtering and tracking. The geometry-based driver allows easy setup of physically meaningful scenarios, including a 3D extended target model (thermal emission and reflected earth, sun, and sky radiance), clutter background, and MODTRAN-based atmospherics. The sensor model accounts for optics, detector, scanning, platform jitter, pattern and sensor noise, and focal plane sampling. Integrated filter and tracker models allow for end-to-end trades and assessing the relative impact of filter and tracker processing. The filter model is a fourier-based ESNR model with a range of filter and registration options. The tracker model is likelihood-based, not simulation or Monte-Carlo, allowing quick identification of dominant effects on performance. Log- likelihood evolves with measurement updates and spreading loss from plant noise, and its statistics characterize optimal tracker performance. Log-likelihood field statistics reveal the effects of suboptimal processing, including covariance misestimation and peak strength thresholding. Various physically meaningful outputs include minimum time to confirm track, ROC curves, and noise exceedance plots. Trade studies generated from this model are presented, illustrating dependencies on scenario, sensor, and signal processing.
