INNOVATIVE ANALYSIS OF SPECTRO-TEMPORAL SIGNATURES USING MACHINE LEARNING FOR GROUND-BASED REMOTE SENSING OF UNRESOLVED RESIDENT SPACE OBJECTS

INNOVATIVE ANALYSIS OF SPECTRO-TEMPORAL SIGNATURES USING MACHINE LEARNING FOR GROUND-BASED REMOTE SENSING OF UNRESOLVED RESIDENT SPACE OBJECTS

PI: Miguel Velez-Reyes

Co-PI: Dan DeBlasio

Sponsor: U.S. SPACE FORCE THROUGH UNIVERSITIES SPACE RESEARCH ASSOCIATION

Electrical and Computer Engineering

Amount awarded: $350,000

This project provides undergraduate and graduate students opportunities to conduct research to advance space domain awareness through providing tactical, predictive, and intelligence information on resident space objects.

Posting date: Tue, 05/03/2022

Award start date: Mon, 05/02/2022

Award end date: Wed, 05/01/2024