We compute AM-FM models for infrared video frames depicting military targets immersed in structured clutter
backgrounds. We show that independent correlation based detection processes can be implemented in the pixel
and modulation domains and used to construct useful online track consistency checks that indicate when the
detection process has been degraded due to nonstationary evolution of the target signature. Throughout the
paper, we use the well-known AMCOM closure sequences as exemplars.
The Electrophysics PV320 is a broadband thermal imaging system with several attractive features including low cost (about USD 25K including optics and software), small size, uncooled operation with a BST sensor array, spectral response from 0.6 to 14 μm, easily interchangeable warm optics, and on board USB 2.0 digital video output. In this paper we describe the technical challenges that were involved in integrating together two copies of the PV320L2Z camera variant to create an experimental dual-band IR data acquisition system for measuring targets, backgrounds, and clutter. The PV320 manufacturer-supplied software includes a user friendly, all-in-one application as well as software development kits providing camera control routines that are callable from C++, Visual Basic, and LabView. While this software works well for operating a single PV320 camera, it does not provide any direct support for simultaneously imaging with multiple cameras. The main technical issues are that the base software driver can connect to only one camera at a time and that multiple instances of the driver cannot be loaded simultaneously. Therefore, to achieve our goal of acquiring dual-band IR signatures, it was necessary to program a custom distributed algorithm capable of running two copies of the driver simultaneously on two separate computers with one PV320L2Z connected to each.
We compute joint AM-FM models that characterize infrared targets and backgrounds in the modulation domain. We consider spatially localized structures within an IR image as sums of nonstationary, quasi-sinusoidal functions admitting locally narrowband amplitude and frequency modulations. By quantitatively estimating the modulations that dominate the signal spectrum on a spatially local basis, we obtain a new modulation domain feature vector that can augment the more traditional pixel domain, Fourier spectrum, and multispectral color features that have been used in IR target detection and tracking systems for a long time. Our preliminary studies, based primarily on midwave and longwave missile approach sequences, suggest that IR targets and backgrounds do typically possess sufficient spatially local modulated structure (i.e., texture) for modulation domain techniques to be meaningfully applied. We also present qualitative results strongly indicating that the modulation domain feature vector is a powerful tool for discriminating infrared targets and backgrounds.