Adaptive Filtering Techniques for Low-Cost RF Emitters

Helios Remote Sensing Systems, Inc. will conduct detailed studies, modeling & simulation, simulation environment integration, adaptive filtering and AI/ML signal conditioning to determine system integration requirements. The developed approaches will be applied to the problem of validating RF emissions by using AI/ML and adaptive algorithms to dynamically filter and correct waveform distortions, ensuring that live signals accurately reflect digital models. AI/ML and adaptive filtering will be used to optimize signal integrity, detect anomalies, and adapt in real-time to fluctuating environments.By the end of Phase I, the deliverable will be a well-documented research report that outlines the steps needed to move forward with prototype development, including risk assessments, technical challenges, and a proposed plan for Phase II. In Phase II, Helios will complete the design for production. A prototype will be delivered for demonstration. The prototype will be tested to validate the design against the threshold requirements identified during the Phase I studies.  Helios will work collaboratively with the Government to refine topic objectives to reach a feasible commercial product.