Research Focus
"Imagination is more important than knowledge." — Albert Einstein
Research Vision
My long-term career goal is to contribute to impactful and innovative research in electronics, RF systems, and intelligent sensing technologies.
I see research not just as an academic requirement, but as a long-term career path where I can continuously innovate, collaborate, and push the
boundaries of what’s technically possible. I’m currently preparing to pursue a research-focused master’s program and, eventually, a Ph.D.
Research Area & Focus
My research interests encompass sensing applications and communications, with a particular emphasis on radar systems, RF technologies, signal processing,
and the integration of AI techniques. I also draw on my background in electronics and embedded systems to prototype intelligent, cost-effective sensing platforms
that enhance detection, tracking, and secure data transmission capabilities.
This interdisciplinary approach aligns with the evolving landscape of modern
communication systems and intelligent sensing technologies. I remain open to exploring adjacent research areas that intersect with these domains,
especially where innovation bridges hardware, intelligence, and real-world applications.
My Research Stack
Conference Contributions: UCUR (2025)
At the Utah Conference on Undergraduate Research (UCUR 2025), I delivered my first independent presentation on a low-cost, dual-band radar system for drone detection and tracking. The project integrated software-defined radio (SDR), Doppler-based signal processing, and embedded AI classification to tackle real-world challenges—most notably, differentiating drones from birds in cluttered environments.
My talk focused on the system architecture, early experimental results, and data integration between the RF subsystems. This experience not only sharpened my technical communication and presentation skills but also laid the groundwork for my next research phase—refining the methodology and validating the architecture at a more advanced level.
UCUR 2025 Presentation: Radar System for Drone Detection
Utah Academy Conference Presentation
At the Utah Academy of Sciences 2025, I presented the methodology and hardware setup for a modular dual-radar detection system combining 5.8 GHz SDR-based FMCW radar and a 24 GHz Doppler radar module. The goal was to evaluate the feasibility of a low-cost architecture for drone detection and classification.
Through extensive lab testing, the SDR subsystem successfully demonstrated short-range reflection detection and basic range estimation, validating the signal processing pipeline using GNU Radio and MATLAB. However, the 24 GHz Doppler module failed to detect small RCS targets like drones, despite multiple attempts under varied conditions. The results highlighted key hardware limitations—such as poor SNR and low angular resolution—which made it unsuitable for fine tracking.
While the full system has yet to be validated in field conditions, the experimental work presented confirmed the viability of SDR-based FMCW radar as a flexible foundation for future multi-sensor integration and AI-enhanced classification.
A related paper has been accepted for publication and will appear in the Utah Academy of Sciences Journal in March 2026.
Utah Academy of Sciences 2025 Conference Presentation
Festival of Excellence Presentation (2025)
At the 2025 Festival of Excellence, I showcased a novel 3D localization framework for drone detection using a bistatic SDR-based FMCW radar setup. This approach replaces costly phased array antennas with a pair of low-cost log-periodic antennas (LPAs), enabling accurate 3D position estimation through time-difference-of-arrival (TDoA) and signal phase analysis.
Our goal was to develop a scalable, modular infrastructure for wide-area drone surveillance—offering a cost-effective alternative to traditional radar systems. By leveraging flexible geometry configurations and software-defined signal processing, we demonstrated that phased arrays are not a prerequisite for fine-grained localization. This not only reduces deployment costs but also allows broader coverage by distributing simpler nodes across large environments.
This research addresses a critical gap in the radar community: enabling affordable, real-time drone detection systems without sacrificing accuracy or flexibility.
This solution is particularly valuable for both civilian and defense applications where low-cost, wide-area coverage is essential for real-time aerial threat monitoring.
A research paper based on this work is currently under review and is expected to be published in 2026.
Festival of Excellence Poster Presentation 2025 at SUU