We establish a theoretical model of the Doppler effect in absolute distance measurements using frequency scanning interferometry (FSI) and propose a novel FSI absolute distance measurement system. This system incorporates a basic FSI system and a laser Doppler velocimeter (LDV). The LDV results are used to correct for the Doppler effect in the absolute distance measurement signal obtained by the basic FSI system. In the measurement of a target located at 16 m, a measurement resolution of 65.5 μm is obtained, which is close to the theoretical resolution, and a standard deviation of 3.15 μm is obtained. The theoretical measurement uncertainty is 8.6 μm + 0.16 μm/m Rm (k = 2) within a distance range of 1 m to 24 m neglecting the influence of air refractive index, which has been verified with experiments.

We propose a frequency swept interferometry (FSI)-based absolute distance measurement method that can be used to measure a noncooperative target located at a distance of 10s of m. In this method, an external cavity laser serves as the frequency tuning laser, and a single frequency laser and two acoustic optical modulators (AOMs) are used to measure the optical path difference (OPD) variation during the frequency tuning, which can correct the Doppler effect. A phase-locked loop (PLL) is introduced to synchronize the nonlinearities between the OPD variation measurement signal and the absolute distance measurement signal, improving the signal-to-noise ratio (SNR) of the OPD variation measurement signal. The distance to a noncooperative target located at 15 m is experimentally measured using this method, and a precision of 3.43 μm is obtained.

Abstract. An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell's equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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