2015.B.3.2. Performance of a smartphone based star tracker

Author(s)

Andrey Khorev (1)
Lionel Torres (1)

University of Montpellier, LIRMM, France

Session

B.3

Keywords

attitude control, star tracker, lost-in-space, smartphone

Abstract

Nowadays CubeSat missions grow more and more complicated. Such tasks as telecommunication, Earth observation and astronomy attract attention of nanosatellite developers. One of the main requirements for the success of a complex mission is the precision and reliability of satellite’s attitude determination and control system. Better attitude precision and better stabilization may be achieved by using a star tracker as a main attitude sensor. Star tracker operation is based on capturing images of stars, this method can provide a pointing accuracy better than 1 angular minute.

In the last couple of years several laboratories and companies performed a huge work on star tracker miniaturization, designing and delivering first prototypes that comply with size, mass and power restrictions of a 3U CubeSat. Newly developed miniature star trackers while preserving core functionality are noticeably different compared to existing large-sized star trackers. The differences might be found in their optics, image sensor, algorithms and processing hardware.

Newly developed miniature star trackers have a set of hardware similar to a modern smartphone. At the same time fast improving application program interfaces of smartphone operating systems give developers today a better control over the smartphone internals. That made possible to implement a complete star tracker algorithm in a smartphone application. During the tests, even with an expected overhead of OS stack, lost-in-space task was solved in less than one second. That defined the choice of a smartphone as a hardware platform for star tracker performance study.

In my work I analyze the performance of a polestar algorithm for autonomous attitude determination. By changing capture parameters, such as brightness, resolution etc. the flaws and bottlenecks of the algorithm are exposed. Subsequently, algorithmic and hardware solutions are proposed to mitigate performance losses.