Computationally Efficient Attitude Estimation for Small Satellites Using Integrated Measurements

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 to reduce 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.