A Dispersion Compensation Method Based on Resampling of Modulated Signal for FMCW Lidar

In order to eliminate the nonlinearity in the laser modulation process, the dual-interferometers system is often adopted in the frequency modulation continuous wave (FMCW) laser ranging. However, the dispersion mismatch between the fiber reference interferometer and the measurement interferometer will lead to the decrease in ranging accuracy and resolution. In this paper, a dispersion compensation method based on resampling with a modulated signal is proposed. Since the beat signal of the end face of the delay fiber is not affected by dispersion mismatch, it can be modulated to generate a signal whose phase is proportional to that of the target spatial signal. Then, the modulated signal is regarded as the reference clock to sample the target spatial signal. Thereby, the influence of the dispersion mismatch between the two optical interferometers can be eliminated. In this article, simulation is performed to verify the effect of this method, and an experiment is carried out on the target at the distance of 2.4 m. Experiments show that the full width at half maximum (FWHM) of the distance spectrum after dispersion compensation is consistent with the reflected signal from the end face of the delay fiber, and the standard deviation of multiple measurements reached 10.12 μm.

Optimization of the GPU-based data evaluation for the low coherence interferometry

Optical interferometers as non-contact measurement devices are very desirable for the measurement of surface roughness and topography. Compared to phase shifting interferometers (PSIs) with a limited measurement range and a scan step of maximum λ/4, the optical interferometers like low coherence interferometers (LCIs) evaluating the degree of fringe coherence allow a larger vertical measurement range. Their vertical measurement range is only limited by the scan length allowed by the linear piezo stage and the coherence length of the light source. To evaluate the obtained data for a large range, the common LCIs require much computation time. To overcome this drawback, we present an evaluation algorithm based on the Hilbert-Transform and curve fitting (Levenberg–Marquardt algorithm) using Compute Unified Device Architecture (CUDA) technology, which allows parallel and independent data evaluation on General Purpose Graphics Processing Unit (GPGPU). Firstly, the evaluation algorithm is implemented and tested on an in-house developed LCI, which is based on Michelson configurations. Furthermore, we focus on the performance optimization of the GPU-based program using the different approaches to further achieve efficient and accurate massive parallel computing. Finally, the performance comparison for evaluating measurement data using different approaches is discussed in this paper.