Field measurement campaigns typically deploy numerous sensors having different sampling characteristics for spatial,
temporal, and spectral domains. Data analysis and exploitation is made more difficult and time consuming as the sample
data grids between sensors do not align. This report summarizes our recent effort to demonstrate feasibility of a processing
chain capable of “fusing” image data from multiple independent and asynchronous sensors into a form amenable to
analysis and exploitation using commercially-available tools.
Two important technical issues were addressed in this work: 1) Image spatial registration onto a common pixel grid, 2)
Image temporal interpolation onto a common time base. The first step leverages existing image matching and registration
algorithms. The second step relies upon a new and innovative use of optical flow algorithms to perform accurate temporal
upsampling of slower frame rate imagery. Optical flow field vectors were first derived from high-frame rate, high-resolution
imagery, and then finally used as a basis for temporal upsampling of the slower frame rate sensor’s imagery.
Optical flow field values are computed using a multi-scale image pyramid, thus allowing for more extreme object motion.
This involves preprocessing imagery to varying resolution scales and initializing new vector flow estimates using that
from the previous coarser-resolution image.
Overall performance of this processing chain is demonstrated using sample data involving complex too motion observed
by multiple sensors mounted to the same base. Multiple sensors were included, including a high-speed visible camera, up
to a coarser resolution LWIR camera.
Lidar has been used to track the downwind dispersion of rocket launch contrails and also to determine the particle size
distribution of the primary Al<sub>2</sub>O<sub>3</sub> smoke particles in the contrail. However, the determination of primary particle size from such lidar measurements is complicated by the presence of secondary smoke in the contrail composed of aqueous
hydrochloric acid droplets. In addition, the secondary smoke tends to condense upon the Al<sub>2</sub>O<sub>3</sub> primary smoke particles
in the form of a liquid coating, with the primary smoke particles acting as condensation nuclei. The potential effect of
this liquid coating upon the lidar backscatter return from the rocket contrail is estimated using a standard light scattering
model (BHCOAT) for two-zone core-mantle particles.
The authors recently developed a hyperspectral image output option for a standardized government code designed to predict missile exhaust plume infrared signatures. Typical predictions cover the 2- to 5-m wavelength range (2000 to 5000 cm-1) at 5 cm-1 spectral resolution, and as a result the hyperspectral images have several hundred frequency channels. Several hundred hyperspectral plume images are needed to span the full operational envelope of missile altitude, Mach number, and aspect angle. Since the net disk storage space can be as large as 100 GB, a Principal Components Analysis is used to compress the spectral dimension, reducing the volume of data to just a few gigabytes. The principal challenge was to specify a robust default setting for the data compression routine suitable for general users, who are not necessarily specialists in data compression. Specifically, the objective was to provide reasonable data compression efficiency of the hyperspectral imagery while at the same time retaining sufficient accuracy for infrared scene generation and hardware-in-the-loop test applications over a range of sensor bandpasses and scenarios. In addition, although the end users of the code do not usually access the detailed spectral information contained in these hyperspectral images, this information must nevertheless be of sufficient fidelity so that atmospheric transmission losses between the missile plume and the sensor could be reliably computed as a function of range. Several metrics were used to determine how far the plume signature hyperspectral data could be safely compressed while still meeting these end-user requirements.
The analysis of particles produced by solid rocket motor fuels relates to two types of studies: the effect of these particles on the Earth's ozone layer, and the dynamic flight behavior of solid fuel boosters used by the NASA Space Shuttle. Since laser backscatter depends on the particle size and concentration, a lidar system can be used to analyze the particle distributions inside a solid rocket plume in flight. We present an analytical model that simulates the lidar returns from solid rocket plumes including effects of beam profile, spot size, polarization and sensing geometry. The backscatter and extinction coefficients of alumina particles are computed with the T-matrix method that can address non-spherical particles. The outputs of the model include time-resolved return pulses and range-Doppler signatures. Presented examples illustrate the effects of sensing geometry.