Published: Feb. 21, 2023

Matthew HaymanMatthew Hayman
Project Scientist, Earth Observing Lab, National Center for Atmospheric Research
Wednesday, Mar. 8 | 9:35 a.m. | AERO 114

Abstract: Lidar has been employed for a variety of remote sensing applications including situational awareness for vehicles, 3D surveying and mapping, and planetary measurement and monitoring.  Such sensors have been deployed on a variety of platforms such as cars, aircraft, UAS and satellites.  However the capabilities that can be deployed on these platforms span a broad spectrum, where the most sophisticated and quantitative sensors have typically been constrained to ground and large aircraft based systems with human operators.

To enable high performance, quantitative lidar, the research community has traditionally emphasized strategies toward higher power and larger collection apertures.  At NCAR we have instead focused on hardware designs that enable agile operation and design, with low power, eye-safe, tunable diode laser sources in the near infrared, and low light detection capability, even in high background conditions.  This approach has been demonstrated through the development of the MicroPulse DIAL (MPD) which operates autonomously and performs quantitative measurements of atmospheric properties (water vapor, temperature, aerosols) commonly associated with more expensive, larger lidar architectures.  This technology approach has the potential to enable instrument development into other otherwise difficult regimes and platforms, where size, weight, power, and maintenance can present significant obstacles to developing sensor solutions.  

While the MPD hardware design is key to enabling the sensor’s performance and capability, leveraging more advanced forms of statistical signal processing is essential to expanding the scope of lidar platforms and applications.  This is because such approaches not only improve data product quality of existing instrumentation, but also inform further improvements to the hardware design. By developing and employing regularized maximum likelihood estimation for MPD we have extended the useful altitude range of the sensor by two km, significantly reduced data product noise, demonstrated atmospheric measurements at unprecedented resolution, established better approaches for acquiring photon counting signals, and discovered of a previously unknown error affecting cloud and aerosol lidar.  

In this talk, I will describe how the interplay of intelligent hardware design and signal processing solutions can enable new lidar capabilities and is key to developing viable sensor solutions for some of the most difficult platforms and data products.

Bio: Matthew Hayman is a Project Scientist in the Earth Observing Lab at the National Center for Atmospheric Research (NCAR).  His research interests include developing new hardware and signal processing solutions for lidar and other imaging problems.  After completing an undergraduate degree in electrical engineering in 2005, Matt worked at Boeing Commercial Aircraft on the 787 program until deciding to attend graduate school in 2006.  He received his PhD in electrical engineering from the University of Colorado at Boulder in 2011 and was an Advanced Study Program (ASP) postdoc at NCAR from 2011-2013.  After completing his postdoc, Matt was hired full time at NCAR’s Research Aviation Facility.  He moved to the Remote Sensing Facility lidar group, where he currently works, in 2016.


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