Standard techniques for the estimation of attitude-determination accuracy assume that the input sensor data has uncorrelated, white noise, and that simulators generally provide white noise. However, real sensor noise is generally observed to have more dominant lower-frequency components. Therefore, this project will develop software to simulate colored noise efficiently, test the noise characteristics, and estimate the impact of the noise characteristics on the predicted attitude-determination accuracy. This project will also explore efficient-noise characterization as a means of automatically identifying subtle anomalies in real data. This work is new and will add an important perspective in the understanding of obtainable attitude accuracles with realistic data. It will also provide tools for realistic data simulation, flight-data characterization, and anomaly identification. These tools will be developed on a pc. Standard techniques for the estimation of attitude-determination accuracy assume that the input sensor data has uncorrelated, white noise, and that simulators generally provide white noise. However, real sensor noise is generally observed to have more dominant lower-frequency components. Therefore, this project will develop software to simulate colored noise efficiently, test the noise characteristics, and estimate the impact of the noise characteristics on the predicted attitude-determination accuracy. This project will also explore efficient-noise characterization as a means of automatically identifying subtle anomalies in real data. This work is new and will add an important perspective in the understanding of obtainable attitude accuracles with realistic data. It will also provide tools for realistic data simulation, flight-data characterization, and anomaly identification. These tools will be developed on a pc.
