Simulating Complex Satellites and a Space-Based Surveillance Sensor Simulation

Abstract : Maintaining space situational awareness requires the ability to track earth-bound satellites in order to know and predict their position, movement, size, and physical features. However, there are many satellites in orbit that are simply too small or too far away to resolve by conventional optical imaging. We can use photometric techniques to gather information about the body in question, but the problem comes in how we interpret the light curve data. Light curves are created by measuring the intensity of reflected sunlight off of the object as it passes overhead. The intensity is dependent on a variety of factors to include the size, shape, orientation, and material composition of the satellite. When we attempt to solve the inverse problem for light curves, we are attempting to extract information about these different factors. Forward modeling of photometric light curves provides a way to generate a large amount of data under controlled conditions for working the inverse problem and is an effective way to test Non-Resolved Space Object Identification (NRSOI) techniques. Currently, there are few implementations of such modeling programs, one of which only allows simple geometric shapes with the option of antennas. We present our modification to that existing code to create complex models plus our new code to calculate shadowing on the complex object. Then we show the results from the new model and a comparison to the original tool. The next generation of space surveillance sensors will be on satellites. Space based sensors avoid many of the problems of ground based sensors, such as, waiting for lighting conditions to match satellite passes and a night sky. These sensors are restricted only by sun exclusion angle and line of sight around the Earth. This allows for more effective techniques and a much longer time on target. We present an addition to the existing code to consider a sensor in orbit.