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Phillip Sandborn, Ph.D. 2017

Electrical Engineering
Advisor: Prof. Wu

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Job Interests: Industry R&D, Bay Area preferred

Phillip Sandborn is a PhD Candidate in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, advised by Prof. Ming Wu. He received his B.S. in Electrical Engineering and B.S. in Mathematics, (both in 2012) at the University of Maryland. His current research interests are in the field of LIDAR sensing and, specifically, the development of new architectures and technologies to reduce size, cost, and power consumption of coherent LIDAR systems.

Non-Linear FMCW Lidar Using Resampling Methods for Long Range and High Resolution [BPN721]
Range-finding sensors have applications that span several industries and markets, from metrology to robotic control. Frequency-modulated continuous-wave lidar has been proven effective in providing high depth resolution, but suffers from limited range due to the limited coherence length of tunable laser sources. Implementations typically require expensive lasers with large coherence length or complex feedback to linearize tunable laser sweeps and extend coherence length. Instead, we use resampling methods to linearize laser sweeps and reduce laser phase noise, all in post-processing, thus reducing the need for precision feedback control or expensive tunable laser hardware. We have demonstrated sub-millimeter resolution at free-space distances >20-meters with 1-inch receiving aperture. In addition, we present a demonstration of this technology which approaches reasonable 3D image acquisition speeds with sub-mm depth precision. Use of RAID and FPGA systems can help approach real- time frame-rates for 3D imaging.

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     Last Updated: Mon 2017-Jan-30 08:16:40

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