This presentation describes STAS (space-time analysis stream), a suite of advanced signal processing codes for the detection of dim targets in 'look-down' IR clutter. STAS has been tested on several hundred thousand IR clutter scenes in the NAWC SkyBall Data Base, and has been shown to be very robust. Code modules in the STAS detection stream perform the following functions: (1) calibration, bad-pixel identification and editing; (2) image registration, clutter estimation and clutter subtraction; (3) velocity stacking and matched-filtering. Other code modules in STAS allow synthesis and rework of IR images, to emulate data from arbitrary IR sensors, with arbitrary trajectories over the clutter scene. THe theory and performance of various STAS modules will be described, with emphasis on registration method, fast methods for mapping from the focal plane to the ground, treatment of bad pixels, digital interpolator design/implementation, and matched filter design. Examples of processed IR images will be displayed.
This paper describes an analytic, end-to-end, IR seeker/sensor dynamic performance model developed to facilitate system level design trades and performance analyses for proposed IR missile seeker/sensor systems. The model has been extensively validated against actual simultaneous dual-band IR imagery (midwave 3 - 5 micrometers , and longwave 8 - 10 micrometers ), collected so as to emulate tactical airborne seeker engagement geometries against a variety of backgrounds. Over 20,000 SIR comparison measurements, with both injected and real targets, have been made. Agreement between predicted and measured SIR is typically within 2 - 3 dB, over a wide range of target brightness, background types, atmospheric conditions, and processing algorithm approaches.
This paper describes a method for parametrically characterizing IR clutter for missile seeker applications in terms of a Butterworth model of the power spectral density (PSD). Traditionally, models of the PSD have been characterized by a Gauss-Markov correlation model, or a similar variant. These tend to estimate the overall spectral shape and integrated spectral power (rms) level very well. However, they tend to be dominated by large spatial features, i.e., low wavenumbers, and consequently, tend to underestimate the power present at high wavenumbers. While this is often not a factor in many remote sensing applications, it is a critical issue for missile seekers, as targets are often sub pixel in extent, and hence compete against clutter at high spatial frequencies. The clutter parameterization algorithm performs a fit to a one-dimensional profile taken from a radially average slice through a two-dimensional PSD computed from geometrically and radiometrically corrected airborne dual-band IR imagery. The parameterization is iteratively constrained to optimize the fit at high wavenumbers, and a two-dimensional isotropic model is computed. A description of the parameterization algorithm, as well as a synopsis of model fits to various clutter types are presented. Additionally, a table of recommended global PSD parameters for clutter characterization (by waveband) is presented herein.
The adaptive matched filter was implemented as a spatial detector for amplitude-only or complex images, and applied to an image formed by standard narrowband means from a wide angle, wideband radar. Direct performance comparisons were made between different implementations and various matched and mismatched cases by using a novel approach to generate ROC curves parametrically. For perfectly matched cases, performance using imaged targets was found to be significantly lower than potential performance of artificial targets whose features differed from the background. Incremental gain due to whitening the background was also found to be small, indicating little background spatial correlation. It is conjectured that the relatively featureless behavior in both targets and background is due to the image formation process, since this technique averages together all wide angle, wideband information. For mismatched cases where the signature was unknown, the amplitude detector losses were approximately equal to whatever gain over noncoherent integration that matching provided. However, the complex detector was generally very sensitive to unknown information, especially phase, and produced much larger losses. Whitening under these mismatched conditions produced further losses. Detector choice thus depends primarily on how reproducible target signatures are, especially if phase is used, and the subsequent number of stored signatures necessary to account for various imaging aspect angles.
In this paper, a simple angle matched image formation algorithm is implemented in which an amplitude weighting corresponding roughly to the dominant feature of a man-made target is used to match its angular response. As such, it represents the first step to a complete frequency-angle matched filter image formation method now under development. Using a common set of measured data, comparisons with practical implementations of two conventional imaging/detection techniques studied previously and a single-pixel detection baseline indicate that only the current approach offers significant performance improvement over baseline.