【摘要】 本文介绍了自回归谱分析法中的奇异值分解法(SVD算法)在亚毫米波付里叶变换谱中的应用.SVD算法和前后向最小二乘法(LS算法)相比,它们具有相同的分辨率,但SVD算法能够消除LS算法中出现的假峰和病态的问题,具有更强的抗噪声能力,因而SVD算法比LS算法更加稳定可靠.还讨论了SVD算法的阶数和有效奇异值个数的选择问题.更多还原

【Abstract】 The singular value decomposition (SVD) algorithm for the AR spectral estimates of the SMMW Fourier transform spectroscopy data is presented. The SVD algorithm has the same resolution as the least squares algorithm using forward and backward linear prediction (LS algorithm) has, but the SVD algorithm can avoid the spurious spectral peaks and ill-conditions appeared with the LS algorithm, and suppress the noise component in the data. The SVD algorithm is more stable and reliable than the LS algorithm. The selections of the order and the number of the effective singular values of the SVD algorithm are discussed.更多还原