Compressing infrared spectrum of exhaust plume by wavelets

A study on multivariate calibration for the infrared spectrum of rocket exhaust plume was presented. As samples taken in the data set, the apparent infrared radiative properties of the high-temperature plume flowfield consisted of variable concentrations gas components and were obtained by using a flux method combined with a narrow-band model and Mie theory. The discrete wavelet transformation as a pre-processing tool was carried out to decompose the infrared spectrum and compress the data set. The compressed data regression model was applied to simultaneous multi-component concentrations for determination of the exhaust plume. The compression performance with several wavelet functions at different resolution scales was studied, and the prediction reliability of the compressed regression model was investigated. Numerical experiment results show that the wavelet transform performs an effective compression preprocessing technique in multivariate calibration and enhances the ability in characteristic extraction of the exhaust plume infrared spectrum. Using the compressed data regression model, the reconstructing results are almost identical when compared to the original spectrum, and the original size of the data set has been reduced to about 5% while the computational time needed decreases significantly.