Computationally Efficient Attitude Estimation for Small Satellites Using Integrated Measurements

Date

2024-01-01

Author

Benli, Dogukan
Söken, Halil Ersin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Every year, computer processors are becoming more powerful and efficient. This progress allows for using less power for computing or more complex models for embedded systems, especially in aerospace. Nevertheless, computationally limited platforms still exist, and developing more capable and efficient algorithms for such platforms is an open research problem. This study aims to create and compare methods that reduce the computational load of onboard attitude estimation algorithms that use the Kalman Filter (KF) as the core algorithm. This is important for small satellites that have limited hardware and power consumption. An accurate attitude estimation algorithm that estimates additional parameters like sensor errors in real time can be beneficial for these resource-limited satellites. The study aims at reducing the number of KF updates without sacrificing the estimation accuracy by manipulating the measurements to get slower frequency pseudo-measurements. These measurements are called ”integrated measurements” and replace the original measurements in the filtering algorithm.

Subject Keywords

Attitude estimation, computational load, Estimation, Extraterrestrial measurements, Gyroscopes, integrated measurements, Kalman Filter, Measurement uncertainty, Small satellites, small satellites, Time measurement, Vectors

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195396079&origin=inward
https://hdl.handle.net/11511/110276

Collections

Department of Aerospace Engineering, Article