  Automated 3D pose estimation of satellites and other known space objects is a
critical component of space situational awareness. Ground-based imagery offers
a convenient data source for satellite characterization; however, analysis
algorithms must contend with atmospheric distortion, variable lighting, and
unknown reflectance properties. Traditional feature-based pose estimation
approaches are unable to discover an accurate correlation between a known 3D
model and imagery given this challenging image environment. This paper presents
an innovative method for automated 3D pose estimation of known space objects in
the absence of satisfactory texture. The proposed approach fits the silhouette
of a known satellite model to ground-based imagery via particle filtering. Each
particle contains enough information (orientation, position, scale, model
articulation) to generate an accurate object silhouette. The silhouette of
individual particles is compared to an observed image. Comparison is done
probabilistically by calculating the joint probability that pixels inside the
silhouette belong to the foreground distribution and that pixels outside the
silhouette belong to the background distribution. Both foreground and
background distributions are computed by observing empty space. The population
of particles are resampled at each new image observation, with the probability
of a particle being resampled proportional to how the particle's silhouette
matches the observation image. The resampling process maintains multiple pose
estimates which is beneficial in preventing and escaping local minimums.
Experiments were conducted on both commercial imagery and on LEO satellite
imagery. Imagery from the commercial experiments are shown in this paper.
