HIRAS noise performance improvement based on principal component analysis

Area of Science:

• Optical engineering
• Spectroscopy
• Space instrumentation

Background:

• Space-borne Michelson interferometers are susceptible to noise degradation.
• Mirror jitters around bias tilt angles are a key source of this noise, impacting spectral data quality.

Purpose of the Study:

• To numerically simulate the impact of mirror jitters on Hyperspectral Infrared Atmospheric Sounder (HIRAS) spectra.
• To develop a method for estimating and minimizing spectrally correlated noise in HIRAS data.

Main Methods:

• A numerical model was developed to simulate HIRAS spectra under mirror jitter conditions.
• Principal Component Analysis (PCA) was employed to estimate the random noise component.
• Spectrally correlated noise was isolated by subtracting the random noise from the total noise.

Main Results:

• Mirror jitters were found to primarily generate spectrally correlated noise in HIRAS data.
• PCA effectively estimated the random noise component.
• Minimizing correlated noise through bias tilt angle tuning optimized HIRAS noise performance.

Conclusions:

• Mirror jitters introduce significant correlated noise in space-borne interferometers like HIRAS.
• PCA serves as a valuable diagnostic tool for noise estimation and performance optimization.
• Optimized tuning of bias tilt angles can mitigate noise and improve spectral data quality.