Featured Application: Absolute distance measurement and surface profiling.Abstract: In our frequency scanning interferometry-based (FSI-based) absolute distance measurement system, a frequency sampling method is used to eliminate the influence of laser tuning nonlinearity. However, because the external cavity laser (ECL) has been used for five years, factors such as the mode hopping of the ECL and the low signal-to-noise ratio (SNR) in a non-cooperative target measurement bring new problems, including erroneous sampling points, phase jumps, and interfering signals. This article analyzes the impacts of the erroneous sampling points and interfering signals on the accuracy of measurement, and then proposes an adaptive filtering method to eliminate the influence. In addition, a phase-matching mosaic algorithm is used to eliminate the phase jump, and a segmentation mosaic algorithm is used to improve the data processing speed. The result of the simulation proves the efficiency of our method. In experiments, the measured target was located at eight different positions on a precise guide rail, and the incident angle was 12 degrees. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 µm, and the maximum mean square error of our method was 2.4 µm, which approached the Cramer-Rao lower bound (CRLB) of 0.8 µm.polarization-maintaining fiber to eliminate the influence of tuning nonlinearity. This method is a common and effective method, but it creates a dispersion mismatch problem that results in target peak broadening. Fortunately, many dispersion compensation methods had been proposed and proved efficiently [20][21][22]. In our experiments without cooperate targets, we use an ECL that has been used for five years, so the SNR is low, and mode-hoping signals often occur. These bring new problems: erroneous sampling points and interfering signals. Our paper analyzes the influences of erroneous sampling points and interfering signals, and then an adaptive filtering method is proposed to eliminate the influences completely. In addition, a phase-matching mosaic algorithm and a segmentation mosaic algorithm are adopted to eliminate the phase jump and improve the data processing speed, respectively.The article is organized as follows: Section 2 reviews the principle of the FSI-based system and discusses the measurement errors caused by erroneous sampling points and interfering signals. Then, an adaptive filtering method, a phase-matching mosaic algorithm, and a segmentation mosaic algorithm are proposed to eliminate the erroneous sampling points and phase jump and reduce the data processing time. In Sections 3 and 4, the results of the simulation and experiment prove the efficiency of the methods that were proposed in Section 2. Finally, a brief conclusion is given in Section 5.
