Bayesian and Least Square Method for Temperature Inversion of Adjacent Space Atmosphere Based on Oxygen A-band

This paper shows that the atmospheric temperature profile in near space is inverted based on the simulated data of adjacent radiation in oxygen A-band. Based on the inversion results, the characteristics of two different inversion algorithms, Bayes and least square, are analyzed and compared. Below 80km, the mean inversion errors of the three spectral lines based on Bayes inversion at 761.59, 762.2 and 764.05nm were 5.52, 3.94 and 4.73K, respectively, after adding the noise with a signal-to-noise ratio of 10³. The mean inversion errors of the least square inversion were 10.57, 7.04 and 8.80K, respectively. The mean inversion errors of the three spectral lines based on Bayes were 18.27, 12.18 and 18.27K, respectively, after adding the noise with a signal-to-noise ratio of 10². The mean errors of the least square inversion were 103.18, 68.79 and 85.98K, respectively. Research results show that the inversion method is based on Bayes theory, the inversion results to make use of a priori information constraints and correction, in the case of noisy a more reasonable solution is obtained, which improves the inversion precision and anti-interference ability. It lays a solid foundation for the research and development of the algorithm for detecting the adjacent space atmosphere temperature on board and provides theoretical guidance for increasing the signal-to-noise ratio of spectral instruments to improve the inversion accuracy of temperature.