Artificial Intelligence / Machine Learning (AI/ML)-Based Radar Data Compression
Develop deep learning models (such as autoencoders or transformer-based methods) to compress raw radar data into low-bit representations. The compressed data should be reconstructed with fidelity that preserves the radar’s practical utility for downstream uses like analysis and sensing. The focus is on reducing storage and transmission costs while maintaining performance.
Source
Dates
Eligibility
Keywords
Supporting Links
Documents
Similar Opportunities
-
Joint Radar & Communication Waveforms
U.S. Department of Defense
-
Collaborative Distributed Swarm Radar
U.S. Department of Defense
-
Agentic AI Based Cognitive Radar for GEOINT Mission
U.S. Department of Defense
-
Sensing Algorithms for Bandwidth Efficient Edge Radars (SABER)
U.S. Department of Defense
-
Vision Language Model (VLM) for Synthetic Aperture Radar (SAR) target search and classification from Sensor Independent Complex Data (SICD)
U.S. Department of Defense
Find tens of thousands more opportunities — free
Scout accounts are free. Sign up to browse the full funding database, unlock the solicitation links and documents for this opportunity, and see how well each one fits your organization.
-
Tens of thousands of funding opportunities
Search and filter every grant and solicitation in Scout — all free, no credit card required.
-
Instant Fit Analysis
Let our grant expert AI score how well this opportunity aligns with your organization, strengths, gaps, and how to improve your chances of winning funding.
-
Source links & full documents
Jump straight to the solicitation and download every attachment.
-
Save opportunities & track deadlines
Keep the opportunities that fit in one place and stay ahead of every due date.
Already have an account? Sign In
Trusted by 500+ Happy Customers