Analysis of Star Identification Algorithms due to Uncompensated Spatial Distortion

With the evolution of spacecraft systems, we see the growing need for smaller, more affordable, and robust spacecrafts that can be jettisoned with ease and sent to sites to perform a myriad of operations that a larger craft would prohibit, or that can be quickly manipulated from performing one task into another. The developing requirements have led to the creation of Nano-Satellites, or CubeSats. The question then remains, how to navigate the expanse of space with such a minute spacecraft? A solution to this is using the stars themselves as a means of navigation. This can be accomplished by measuring the distance between stars in a camera image and determining the stars\u27 identities. Once identified, the spacecraft can obtain its position and facing. A series of star identification algorithms called Lost in Space Algorithms (LISAs) are used to recognize the stars in an image and assess the accuracy and error associated with each algorithm. This is done by creating various images from a simulated camera, using a program called MATLAB, along with images of actual stars with uncompensated errors. It is shown how suitable these algorithms are for use in space navigation, what constraints and impediments each have, and if low quality cameras using these algorithms can solve the Lost in Space problem