We present progress being made in the passive optical remote detection of ground surface vibration. With proper design, minute seismic surface waves may be captured using remote visible imagery. The utility of subband steerable filters to the detection of surface vibrations in the absence of inherent image contrast is demonstrated. Detections with the filters are shown with laboratory data and compared to Fourier transform results over a range of surface vibrational amplitudes. We present an analysis of the optical measurements of ground surfaces performed during the passing of nearby trains with discussion of the hardware, software, and detection clutter sources. Results from optical remote sensing are interpreted using additional accelerometer measurements and image processing.
The Air Force Weather Agency (AFWA) has a long history of providing global cloud analyses and forecasts. Until recently, their focus has been on determining the cloud amount and cloud type. Satellite-based World-Wide Merged Cloud Analysis (WWMCA) data provided by the AFWA are analyzed to understand and assess their capability to characterize cloud single scattering parameters at optical wavelengths. WWMCA represents the most refined version of AFWA’s cloud depiction and forecast system and includes up to four cloud layers and 38 cloud parameters per file at each hemispheric grid point. Findings on WWMCA’s determination of cloud optical depth (COD), consistency with synoptic-scale cloud fields, and its ability to support radiative transfer calculations are as follows: (1) the WWMCA optical depth is strongly correlated with the theoretical optical depth at 550 nm computed using WWMCA’s cloud microphysical parameters. (2) WWMCA captures the large-scale spatial variation of COD as represented by the Mei-yu/Baiu onset and progression of synoptic cloud fields as well as the time-dependent character of mesoscale features. (3) WWMCA does not provide single scattering albedo and the cloud phase function which are needed to solve the radiative transfer equation. Because WWMCA is based on passive sensor processing, obscured cloud layers are not accounted for in the calculation of COD, which may lead to an underestimation of total COD.
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.
We report on a passive imaging technique to measure physical properties of a vibrating surface using the detection of optical signal modulation in light scattered from that surface. The optical signal modulation arises from a changing surface normal and may be used to produce a surface normal change image without touching the surface and changing its state. The images may be used to extract the surface vibration frequency and mode pattern which are dependent on surface properties of the material, including its flexural modulus and mass density. Comparison of the vibration image with a finite element model may be used to infer properties of the vibrating surface, including boundary conditions. A temporal sequence of optical images of signal modulation may be analyzed to infer spatial damping properties of the surface material. Damping is a measure of energy dissipation within the material. The approach being developed has the advantage of being able to remotely image arbitrary sized structures to determine global or local vibrational properties.
High-framerate imaging enables the long-range characterization of the vibration modes and amplitudes of a passively
illuminated structure. The vibration signature arises from modulation of the bidirectional reflection distribution function
(BRDF) as the surface normal oscillates with respect to a fixed, directional source. In this paper we consider the
instrument design characteristics and environmental factors that limit passive vibrometer performance including BRDF
angle sensitivity, receiver spatial resolution, interference from atmospheric scintillation, and intrinsic detector
performance. We here identify a sensor architecture that is capable of characterizing surface vibration at amplitudes
below 1 mrad root mean square (RMS) and discuss detector technology that can further improve long-range vibrometer
Detection and tracking of dim targets in heavy clutter environments is a daunting theoretical and practical problem.
Application of the recently developed Background Agnostic Cardinalized Probability Hypothesis Density (BA-CPHD)
filter provides a very promising approach that adequately addresses all the complexities and the nonlinear nature of this
problem. In this paper, we present analysis, derivation, development, and application of a BA-CPHD implementation for
tracking dim ballistic targets in environments with a range of unknown clutter rates, unknown clutter distribution, and
unknown target probability of detection. The effectiveness and accuracy of the implemented algorithms are assessed and
evaluated. Results that evaluate and also demonstrate the specific merits of the proposed approach are presented.
Hyperspectral imaging sensors operating in the visual, near IR, and thermal IR bands are sufficiently advanced to
become a standard component of surveillance sensor suites. The output of these sensors contains a wealth of spectral
and spatial information that can improve target detection and recognition performance. However, the large volume and
complex features of hyperspectral data are challenges to automatic target recognition (ATR) algorithm development, and
a simulation of hyperspectral sensing is therefore essential in evaluating algorithm performance. This paper describes
the Infrared Hyperspectral Scene Simulation (IRHSS), an accurate, non-real-time large-scene simulation tool for
hyperspectral imagers operating in the thermal IR bands. The simulation contains models for target and background
spectral radiance, atmospheric propagation, and sensor processing. It uses a new hyperspectral version of the Multi-service
Electro-optical Signature (MuSES) model to compute scene temperatures and hyperspectral radiances. IRHSS
is able to handle very large terrain and feature databases by selective use of radiation view factors. It provides end-to-end
simulation starting with scene models built from COTS simulation databases with faceted terrain and targets, and
optional overlays of visual high-resolution texture imagery. IRHSS can be run as a standalone application via its
Windows-based graphical user interface (GUI) or as a plug-in to existing software using the IRHSS application
programming interface (API). Some screen images of the IRHSS GUI and example hyperspectral image cubes
generated by IRHSS are included herein.
In recent years, military operations have seen an increasing demand for high-fidelity predictive ground target signature
modeling in the hyperspectral thermal IR bands (2 to 25 μm). Simulating hyperspectral imagery of large scenes has
become a necessary component in evaluating ATR algorithms due to the prohibitive costs and the large volume of data
amassed by multi-band imaging sensors. To address this need, MuSES (Multi-Service Electro-optic Signature code), a
validated infrared signature prediction program developed for modeling ground targets, has been enhanced to compute
bi-directional reflectance distribution radiances and atmospheric propagation hyperspectrally, and to generate
hyperspectral image data cubes. In this paper, we present the extensions in MuSES and report on how the additional
features have allowed MuSES to be integrated into the Infra-Red Hyperspectral Scene Simulation (IRHSS), a scene
simulation tool that efficiently models sensor-weighted hyperspectral imagery of large IR synthetic scenes with full
thermal interaction between the target and terrain.