Modeling and Detecting Orbit Observation Errors Using Statistical Methods
thesisposted on 15.06.2020, 16:56 by Christopher Y Jang
In the globally collaborative effort of maintaining an accurate space catalog, it is of utmost importance for ground tracking stations to provide observations which can be used to update and improve the catalog. However, each tracking station is responsible for viewing thousands of objects in a limited window of time. Limitations in sensor capabilities, human error, and other circumstances inevitably result in erroneous, or unusable, data, but when receiving information from a tracking station, it may be difficult for the end-user to determine a data set's usability. Variables in equipment, environment, and processing create uncertainties when computing the positions and orbits of the satellites. Firstly, this research provides a reference frame for what degrees of errors or biases in equipment translate to different levels of orbital errors after a least squares orbit determination. Secondly, using just an incoming data set's angle error distribution compared to the newly determined orbit, statistical distribution testing is used to determine the validity and usability of the newly received data set. In the context of orbit position uncertainty, users are then able to communicate and relay the uncertainties in the data they share while assessing incoming data for potential sources of error.