Lunasa Ltd. (Lúnasa) is a UK-headquartered SpaceTech startup founded on the 1st of March 2021.  The company is revolutionising space exploration by empowering intelligence into spacecraft navigation systems. Its first product is an AI-based Rendezvous Proximity Operations (RPO) technology that enables the execution of complex missions from satellite refueling and active debris removal to cargo transportation to space stations and deep space planetary exploration. Lúnasa has been backed by top-tier deep-tech private investors and has achieved several industry awards from leading organisations such as the UK Space Agency and the European Space Agency. With a Team of 8 and growing, Lúnasa has built expertise in AI/ML, satellite navigation, space systems, and much more.

Project Description:

The project encompasses several key steps to enhance satellite orbit determination accuracy for high-performance positioning services. The primary objective is to develop stable and reliable high-precision satellite orbit products, with a specific focus on Precise Point Positioning (PPP) in absolute positioning modes.

The project will explore two main methods for real-time Precise Orbit Determination (POD): ultra-rapid orbit prediction and real-time filtering orbit determination. The main challenges include refining satellite dynamic stochastic models, adaptive filtering for irregular satellite motions, rapid convergence, and real-time Ambiguity Resolution (AR).

To start the project, a thorough literature review will be conducted, summarizing current research progress in real-time filtering POD and highlighting key issues within the field. The intern will then proceed to develop real-time filtering orbit determination software tailored to overcome the identified challenges and enhance the stability and reliability of satellite orbit products.

The performance of the developed software will be evaluated with a specific focus on the 3D real-time orbit accuracy from GPS and Galileo satellites, to achieve accuracy levels better than 5 cm with AR. A comparative analysis between the convergence time and accuracy of kinematic PPP AR for filter orbit products and ultra-rapid orbit products validates the superior performance of the filter orbit products.

Applicant Specification:

We are looking for a forward-thinking, high-caliber, and motivated candidate to work on an innovative GNC system for space proximity operations and docking applications. The intern must be comfortable with working in a team under agile development methods, interacting with researchers and engineers, able to perform high-quality research in collaboration with the other team members, and should not yield under stress.

Academic: A Bachelor's/Master’s degree in Space or Aerospace engineering, with at least some coursework/experience in the areas of astrodynamics and aerospace vehicle design.

Minimum requirements:

• Understanding of the engineering problems related to spacecraft design and the related technologies.

• Experience with numerical simulation and the mathematics behind it, especially in physical and space trajectory simulations.

• Coding skills in MATLAB, Simulink, and Python.

• Excellent oral and written communication skills.

Desirable requirements:

• Experience with 6-degree-of-freedom spacecraft dynamics and simulation environments.

• Understanding of the engineering problems related to guidance, navigation, and control for spacecraft, including a basic grasp of absolute and relative navigation concepts and technologies.

Process details:

• 12 weeks minimum fixed term contract to be agreed with the successful candidate