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Imaging IR devices, particularly two-dimensional staring FPAs, are capable of providing large amounts of data applicable to target detection and tracking. This information is received as dynamic imagery of the subject scene, with commercial sensors currently delivering digital data at rates above 35 megabits per second. Currently, due to these high data rates, system performance is limited by the processor throughput and performance. Efficient methods are needed to suppress the deleterious effects of clutter and noise in order to achieve operability against low-signature targets in high-clutter conditions while maintaining real-time operation. To accommodate this, fundamental Wiener filter concepts were applied to design several digital processing concepts. These concepts used noise models that were based on recent progress in representing background clutter. Although not implemented, the general technique is applicable to additional disturbances such as fixed-pattern noise, assuming the noise power spectrum is known. The IR background was modeled using a three-parameter Gauss-Markov power spectral density. Four digital processors were designed using finite impulse response (FIR) kernels of varying size, assuming a fixed-size circular target in each case. A simple band-pass kernel tuned to the target was also considered as a baseline non-Wiener filter. These filters were compared based on their realized transfer functions compared to the ideal Wiener transfer function. The detection processing was accomplished by convolving single-frame imagery with the FIR kernels. Using the filtered imagery, varying thresholds were applied to derive the receive operating curve relating detection and false-alarm probabilities. The processor details and design parameters derived from the noise and clutter parameters are described. Empirical and analytical assessments of filter performance were obtained in clutter backgrounds. Applicability to real-world IR backgrounds is demonstrated through evaluation against real scenes. The analytical model supports assessments of processor performance against extended targets, including quantifying the performance penalties due to imposition of realistic processing constraints.
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