Mr. Berken Utku Demirel Profile

Berken Utku Demirel

at UCI

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Proceedings Article | 15 March 2023 Presentation + Paper

Optical ranging and localization at beyond the coherence length of lasers

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

Proceedings Volume 12438, 124380H (2023) https://doi.org/10.1117/12.2647159

KEYWORDS: Laser frequency, Continuous wave operation, Detection and tracking algorithms, Data modeling, LIDAR, Error analysis, Phase shift keying, Education and training, Performance modeling, Ranging

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Long-distance ranging in existing coherent lidar techniques suffer from the coherence length of lasers. Here we present a coherent multi-tone continuous-wave (MTCW) lidar technique that performs single-shot simultaneous ranging and velocimetry with a high resolution at distances far beyond the coherence length of a CW laser, without frequency/phase sweeping. The proposed technique utilizes relative phase accumulations at phase-locked RF sidebands and Doppler shifts to identify the range and velocity of the target after a heterodyne detection of the beating of the echo signal with an unmodulated CW optical local oscillator (LO). The predefined RF sidebands enable ultra-narrow-bandwidth RF filters in the analog or digital domain to suppress noise and achieve high SNR ranging and velocimetry. Up-to-date, we demonstrated that the MTCW-lidar could perform ranging ×500 beyond the coherence length of the laser with <1cm precision. In a quasi-CW configuration, >1km ranging is realized with <3cm precision. Moreover, we incorporate machine-learning algorithms into MTCW-lidar to identify the reflections from multiple targets and improve the range resolution. Since relative phases of RF-sidebands are utilized for ranging, and common phase noises can be suppressed in signal processing, we show that the LO in heterodyne detection does not have to be the same laser source. Hence a separate free-running laser can be used. This approach paves the way for novel optical localization. To prove the concept, we present that a receiver with a free-running CW LO can determine its relative distance to a remote transmitter at 1.5km away with a <5cm accuracy.

Proceedings Article | 8 March 2023 Presentation + Paper

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, 124280I (2023) https://doi.org/10.1117/12.2650368

KEYWORDS: Laser frequency, Continuous wave operation, Detection and tracking algorithms, Data modeling, LIDAR, Error analysis, Machine learning, Education and training, Performance modeling, Ranging

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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.

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