Application of a Multiple Hypothesis Filter to near GEO high area-to-mass ratio space objects state estimation

Optical surveys have identified a class of high area-to-mass ratio (HAMR) objects in the vicinity of the Geostationary Earth Orbit (GEO) regime. The nature of these objects is not well known, though their proximity to the GEO belt implies origins from space objects (SOs) near GEO. These HAMR objects pose a collision hazard as they transit through the vicinity of active GEO satellites. Due to their high area-to-mass ratios (AMRs), ranging from 0.1 to 20 m²/kg and higher, the effective solar radiation pressure perturbs their orbits significantly. Improvements in detection sensitivity will result in large numbers of uncorrelated tracks from surveys. A Multiple Hypothesis Filter (MHF) approach to the initial state estimation and track association provides a potentially automated and efficient approach to the processing of multiple un-correlated tracks.The availability of long-term optical angles data collected for a set of near GEO HAMR objects provides the means for testing candidate estimation processes such as the MHF. A baseline orbit determination (OD) process uses an Extended Kalman Filter/Smoother to manually estimate the 6 orbital elements and the effective area-to-mass ratio (AMR) which drives the solar radiation pressure perturbations on the orbital trajectories. In addition to allowing the characterization of the long-term behavior of the AMR, this process establishes a pseudo-truth trajectory to which the MHF analysis can be compared. An Unscented Kalman Filter (UKF) is applied in the MHF estimation process to estimate the 6 orbital elements and AMR, with no a priori state assumptions, and the results are compared to the pseudo-truth results for validation.The work to be presented summarizes the UKF/MHF process and assesses state estimation performance based on selected data for selected near GEO HAMR objects having a range of AMR value and variations. The prediction accuracy is also assessed by comparing predictions derived from filter updates to segments of the pseudo-truth trajectory determined from data not included in the updates.     

Reconciling space object observed and solar pressure albedo-areas via astrometric and photometric data fusion

There are many Resident Space Objects (RSOs) in the Geostationary Earth Orbit (GEO) regime, both operational and debris. The primary non-gravitational force acting on these RSOs is Solar Radiation Pressure (SRP), which is sensitive to the RSO’s area-to-mass ratio. Sparse observation data and mismodeling of non-gravitational forces has constrained the state of practice in tracking and characterizing RSOs. Accurate identification, characterization, tracking, and motion prediction of RSOs is a high priority research issue as it shall aid in assessing collision probabilities in the GEO regime, and orbital safety writ large. Previous work in characterizing RSOs has taken a preliminary step in exploiting fused astrometric and photometric data to estimate the RSO mass, shape, attitude, and size. This works, in theory, since angles data are sensitive to SRP albedo-area-to-mass ratio, and photometric data are sensitive to shape, attitude, and observed albedo-area. By fusing these two data types, mass and albedo-area both become observable parameters and can be estimated as independent quantities. However, previous work in mass and albedo-area estimation has not quantified and assessed the fundamental physical link between SRP albedo-area and observed albedo-area. The observed albedo-area is always a function of the SRP albedo-area along the line of sight of the observer. This is the physical relationship that this current research exploits. It is shown through simulation that due to this physical link, and through the fusion of astrometric and photometric data, it is possible to observe the mass of a space object when the area is not known. Results for data from 100 trajectories generated from randomly sampled initial conditions are shown. It is seen that even when the area of the object is not known, the uncertainty in mass can be lowered from an initial value of 800?kg to the range 500–700?kg for 72% of the samples, 200–500?kg for 13% of the samples, and 0–200?kg for 15% of the samples. It is further shown that although the uncertainties are large, the actual errors in mass are much lower, with the error RMS being less than 100?kg for 30% of the samples, between 100 and 200?kg for another 30%, and between 200 and 300?kg for 24% of the samples.