Space-Debris Data Association

Infographic courtesy NASA-JPL

Ever since man discovered the ability to launch artificial satellites, the number of satellites in space has only been growing. The advent of budget-friendly satellites like Nano-sats, cube-sats, and chip-sats is giving many organization and universities the ability to launch inexpensive space missions. These small satellites are launched into low earth orbit, and they add to the already present functional and defunct resident space objects (RSO). The space environment is getting more and more cluttered and thereby endangering the space assets already in space. Thus space situational awareness has been gaining importance..

Space situational awareness (SSA) if of paramount importance to ensure safety of life and equipment. SSA is achieved by actively tracking and identifying space debris.

Closely-spaced objects, especially debris objects, create a setting that is very similar to a multi-target environment in a tracking problem. This environment engenders a major data association problem in the field of space situational awareness. To address this problem, an approach that couples gating methods for data association along with a star pattern recognition algorithm called the planar triangular method, is developed. The work in this paper shows the effectiveness of combining traditional data association methods with an existing planar triangle pattern recognition algorithm for space object association.

To simulate realistic conditions, the data association algorithm was tested on data from the debris field of the 2007 Chinese anti-satellite missile test of the Fengyun 1C satellite. This Chinese weather satellite was launched into a Sun-synchronous orbit with a mean altitude of 850 km and an inclination of 98.8 degrees.

For more details on the technical aspects of the gating-based planar triangle method for space object association please read the paper.