Robust Attitude Uncertainty Estimation for Debris Removal (Expro+) Tender, Germany - 99122964

Procurement Summary

Country : Germany

Summary : Robust Attitude Uncertainty Estimation for Debris Removal (Expro+)

Deadline : 22 Apr 2024

Other Information

Notice Type : Tender

TOT Ref.No.: 99122964

Document Ref. No. : 1-12203

Competition : ICB

Financier : Other Funding Agencies

Purchaser Ownership : Public

Tender Value : Refer Document

Purchaser's Detail

Purchaser : EUROPEAN SPACE OPERATIONS CENTRE
Germany Tel: +49 6151 90 2000
Germany

Tender Details

Tenders are invited for Robust Attitude Uncertainty Estimation for Debris Removal (Expro+)

Price Range 200-500 KEURO

Active debris removal (ADR) and in-orbit servicing (IOS) missions rely on spacecraft attitude information. Attitude motion estimates are key drivers for selecting removal targets and for the planning of removal missions (e.g. close proximity operations) and the design of the spacecraft (e.g. the capturing mechanism).Attitude states are currently determined from optical light curves, laser ranging residuals and radar imaging. Novel approaches must be explored to get a more comprehensive picture, e.g. using RCS time series, diffusely scattered laser returns and data from photon counting devices. Current tools and algorithms do not estimate the uncertainty associated to the determined attitude state. This information is crucial for the derivation of safety marginsand operational procedures for rendezvous and capture. Uncertainty estimates further allow data fusion of observations from different sources with complementary information content, which increases the overall confidence in the obtained solution.The goal is to develop an attitude and uncertainty determination algorithm for objects with and without a-priori knowledge (e.g. a method based on diffusely reflected laser pulses, retroreflector array signature of laser ranging residuals, light curve simulation and fitting using geometric models for well-known targets), or alternative approaches assuming simplified shapes for other targets. The algorithm shallconsider novel measurement types and account for uncertainties caused by model inaccuracies/simplifications and by input observation uncertainties. Iterative improvement methods (e.g. sequential filters) shall be studied to model complex attitude dynamic evolution (e.g. nutation and precession of spin axis direction), to reduce the search space and therefore uncertainty region when acquiring new observational data. To validate the developed algorithms, it is fundamental to enhance the performances of the attitude simulation software considering new sensor and measurement types.

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