Machine-learning-enhanced phase-based multi-tone continuous-wave lidar

M. Mert Bayer; Berken Utku Demirel; Ataberk Atalar; Xun Li; Haoyu Xie; Ozdal Boyraz

doi:10.1117/12.2650368

8 March 2023 Machine-learning-enhanced phase-based multi-tone continuous-wave lidar

M. Mert Bayer, Berken Utku Demirel, Ataberk Atalar, Xun Li, Haoyu Xie, Ozdal Boyraz

Proceedings Volume 12428, Photonic Instrumentation Engineering X; 124280I (2023) https://doi.org/10.1117/12.2650368
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Lidar technologies have been investigated and commercialized for various applications such as autonomous driving and aerial vehicles. The pulsed time of flight and frequency-modulated continuous-wave lidars are the two common lidar technologies that dominate. As an alternative to the available lidars, we developed the phase-based multi-tone continuouswave (PB-MTCW) technology that can perform single-shot simultaneous ranging and velocimetry measurements with a high resolution at distances far beyond the coherence length of a CW laser, without employing any form of sweeping. The proposed technique utilizes relative phase accumulations at phase-locked RF sidebands to identify the range of the target after a heterodyne detection of the beating of the echo signal with an unmodulated CW optical local oscillator (LO). Upto-date, we demonstrated that the PB-MTCW lidar could perform ranging ×500 beyond the coherence length of the laser with <1cm precision. Here, we implement machine learning (ML) algorithms to the PB-MTCW architecture to improve the ranging resolution, as well as to provide a solution to multi-target reflections using tone-amplitude variations. We used four different training schemes by utilizing the acquired RF tones and phases from simulation results, experimental results, and their combinations in a convolutional neural network model. We demonstrate that the ML algorithm yields an average mean square error of ~0.3mm compared to the actual target distance, hence enhancing the ranging resolution of PB-MTCW lidar. It is also shown that the ML algorithm can distinguish multiple targets in the same line of sight with a 98%±0.7% success rate depending on the targets’ reflectance and distances.

Conference Presentation

Citation Download Citation

M. Mert Bayer, Berken Utku Demirel, Ataberk Atalar, Xun Li, Haoyu Xie, and Ozdal Boyraz "Machine-learning-enhanced phase-based multi-tone continuous-wave lidar", Proc. SPIE 12428, Photonic Instrumentation Engineering X, 124280I (8 March 2023); https://doi.org/10.1117/12.2650368

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