We discuss an algorithmic approach for detecting spatially stationary, dim signals in cluttered optical data. In the problem considered here, cluttered scene backgrounds are substantially more intense than sensor noise and signal variations from scene anomalies of interest. As a result, clutter estimation and rejection algorithms are performed prior to implementing signal detection schemes. Even then, stationary residual clutter may be spatially similar to, and have intensities much greater than, those of the signals of interest. This poses an extreme challenge for the automated detection of low-contrast scene anomalies, and detectors based solely on spatial properties of the optical scene generally fail. In our newly developed signal detection algorithm, we exploit not only the structure of the dim signals of interest, but also the time-lapsed residual clutter. By examining the properties and statistics of both the signals of interest and the signals we wish to reject, Toyon has developed an algorithm for the automated detection of low-contrast signals in the presence of high-intensity clutter. We discuss here the developed signal detection algorithm and results for overcoming the challenges inherent to heavily cluttered optical data.
We have performed research to understand the feasibility of using signals received by EOIR sensors to detect small vibrations in surfaces illuminated by sunlight. The vibration models consider buildings with vibrating roofs, as well as ground vibrations due to buried structures. For the surface buildings, we investigated two approaches. One involved treating the roof as an elastic medium subject to deformation resulting in a PDE whose solution describes the fluctuation in the surface’s normal direction vector. The second approach treated the roof as a rigid mass subject to motion in six degrees of freedom, while modeling the dynamics of the building’s frame, and tuning the parameters to result in resonant frequencies similar to real buildings (~3-7 Hz). We applied the appropriate physical models of reflected and scattered light to various surfaces, specular (insulator or conductor), rough but still reflective, or diffusely scattering (Lambertian). Matlab code was developed to perform numerical simulations of any system configuration described above and easily add new models. The main engine of the code is a signal calculator and analyzer that sums the total intensity of received light over a “scene” with a variety of surface materials, orientations, polarization (if any), and other parameters. A resulting signal versus time is generated that may be analyzed in order to: 1) optimize sensitivity, or 2) detect the vibration signature of a structure of interest. The results of this study will enable scientists/engineers to optimize signal detection, possibly from space, for passive exploitation of scattered light modulated by vibrating surfaces.