Predicting the expected amount of observable space debris with an SSA capable star tracker

The Earth’s space environment is becoming increas-
ingly congested. To aid in the effort of space situational aware-
ness (SSA), a comprehensive sensor network is required to
accurately map the spatial distribution of resident space objects
(RSOs). Star trackers, typically used for optical attitude deter-
mination, are ideal candidates for dual-use as opportunistic SSA
sensors due to their inherent capabilities.
A fast and reliable methodology to estimate the amount of debris
an optical sensor can detect is crucial. However, the vastness and
variability of the space environment complicate this task. Given
the scale and diversity of space-based objects, simulations are
often employed but are computationally intensive and limited in
scope, with a strong stochastic component.
This paper presents a novel method for reliably predicting the
expected RSO signal detectable within the field of view of a
generalized optical sensor. By leveraging the governing radio-
metric equations and key geometrical constraints, individual
particles are modeled to determine the photoelectron flux on
the sensor’s detector plane. These particles, weighted by their
spatial density, are integrated over the conical volume defined by
the sensor field of view. Using importance-sampled Monte Carlo
integration, this method achieves comprehensive and accurate
representations of the debris environment with relatively few
samples. Additionally, a reliable estimate is formed for the
precision of this approach.
This customizable algorithm offers valuable insights during the
preliminary design of new missions and serves as a robust tool
for cross-validation of real-world data. Its features are demon-
strated for both a space-based and ground-based scenario.