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  • Using Machine Learning to Improve Forecasting of Deep Convection

    PI Christopher Hennon

    CO-I Ronny Schroeder

    CO-I Curtis James

    CO-I Abd AlRahman AlMomani

    We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
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    Tags: thunderstorms monsoon machine learning forecasting tool

    Categories: Faculty-Staff

  • ICARUS Drone Net

    PI Samuel Siewert

    CO-I Iacopo Gentilini

    CO-I Mehran Andalibi

    CO-I Stephen Bruder

    Drone Net is a conceptual architecture to integrate passive sensor nodes in a local sensor network along with traditional active sensing methods for small Unmanned Aerial Systems traffic management. The goal of the proposed research architecture is to evaluate use of multiple passive sensor nodes integrating Electro-Optical/Infrared and acoustic arrays networked around a UAS Traffic Management operating region (Class G uncontrolled airspace). The Drone Net approach will be compared to and/or used in addition to RADAR and Automatic Dependent Surveillance-Broadcast tracking and identification.  We hypothesize that this approach can better manage non-compliant small UAS along with compliant UAS and general aviation in sensitive airspace, urban locations, and geo-fenced regions. 


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    Tags: machine vision machine learning UAS UTM ADS-B LIDAR RADAR EO/IR acoustic localization navigation control C4ISR

    Categories: Faculty-Staff

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